ACS Publications. Most Trusted. Most Cited. Most Read
Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes
More

OR SEARCH CITATIONS

My Activity
Publications

Figure 1Loading Img
Download Hi-Res ImageDownload to MS-PowerPointCite This:ACS Cent. Sci. 2018, 4, 8, 996-1006
  • Open Access
Research Article

Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes
Click to copy article linkArticle link copied!

  • Zeeshan Ahmad
    Zeeshan Ahmad
    Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
    More by Zeeshan Ahmad
    Orcidhttp://orcid.org/0000-0001-9758-8952
  • Tian Xie
    Tian Xie
    Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
    More by Tian Xie
    Orcidhttp://orcid.org/0000-0002-0987-4666
  • Chinmay Maheshwari
    Chinmay Maheshwari
    Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
    More by Chinmay Maheshwari
  • Jeffrey C. Grossman
    Jeffrey C. Grossman
    Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
    More by Jeffrey C. Grossman
  • Venkatasubramanian Viswanathan*
    Venkatasubramanian Viswanathan
    Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
    Department of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
    *E-mail: [email protected]
    More by Venkatasubramanian Viswanathan
    Orcidhttp://orcid.org/0000-0003-1060-5495
Open PDFSupporting Information (5)

ACS Central Science

Cite this: ACS Cent. Sci. 2018, 4, 8, 996–1006
Click to copy citationCitation copied!
https://doi.org/10.1021/acscentsci.8b00229
Published August 10, 2018

Copyright © 2018 American Chemical Society. This publication is licensed under these Terms of Use.

Request reuse permissions

Abstract

Click to copy section linkSection link copied!
High Resolution Image
Download MS PowerPoint Slide

Next generation batteries based on lithium (Li) metal anodes have been plagued by the dendritic electrodeposition of Li metal on the anode during cycling, resulting in short circuit and capacity loss. Suppression of dendritic growth through the use of solid electrolytes has emerged as one of the most promising strategies for enabling the use of Li metal anodes. We perform a computational screening of over 12 000 inorganic solids based on their ability to suppress dendrite initiation in contact with Li metal anode. Properties for mechanically isotropic and anisotropic interfaces that can be used in stability criteria for determining the propensity of dendrite initiation are usually obtained from computationally expensive first-principles methods. In order to obtain a large data set for screening, we use machine-learning models to predict the mechanical properties of several new solid electrolytes. The machine-learning models are trained on purely structural features of the material, which do not require any first-principles calculations. We train a graph convolutional neural network on the shear and bulk moduli because of the availability of a large training data set with low noise due to low uncertainty in their first-principles-calculated values. We use gradient boosting regressor and kernel ridge regression to train the elastic constants, where the choice of the model depends on the size of the training data and the noise that it can handle. The material stiffness is found to increase with an increase in mass density and ratio of Li and sublattice bond ionicity, and decrease with increase in volume per atom and sublattice electronegativity. Cross-validation/test performance suggests our models generalize well. We predict over 20 mechanically anisotropic interfaces between Li metal and four solid electrolytes which can be used to suppress dendrite growth. Our screened candidates are generally soft and highly anisotropic, and present opportunities for simultaneously obtaining dendrite suppression and high ionic conductivity in solid electrolytes.

Copyright © 2018 American Chemical Society

Subjects

what are subjects

Synopsis

Driven by machine-learning methods, we perform a large-scale screening of ∼13 000 materials for their use as solid electrolytes in Li metal anode-based batteries.

Introduction

Click to copy section linkSection link copied!
Increased energy densities of Li-ion batteries are crucial for progress toward complete electrification of transportation. (1−3) Among the many possible routes, Li metal anodes have emerged as one of the most likely near-term commercialization options. (4) Coupled with a conventional intercalation cathode, batteries utilizing Li metal anodes could achieve specific energy of >400 W h/kg, much higher than the current state of the art ∼250 W h/kg. (5,6) Unstable and dendritic electrodeposition on Li metal anode coupled with capacity fade due to consumption of electrolyte has been one of the major hurdles in its commercialization. (5,7−12) For large-scale adoption, a stable, smooth and dendrite-free electrodeposition on Li metal is crucial.
Numerous approaches are being actively pursued for suppressing dendrite growth through the design of novel additives in liquid electrolytes, (13−19) surface nanostructuring, (20,21) modified charging protocols, (22,23) artificial solid electrolyte interphase or protective coatings, (24−26) polymers, (27−29) or inorganic solid electrolytes. (30−33) Among these, dramatic improvements in the ionic conductivity of solid electrolytes (34,35) have made them extremely attractive candidates for enabling Li metal anodes.
A comprehensive and precise criterion for dendrite suppression is still elusive. Interfacial effects (36,37) and spatial inhomogeneities within the solid electrolyte like voids, grain boundaries, and impurities (38) make the problem challenging. Monroe and Newman performed a dendrite initiation analysis and showed that solid polymer electrolytes with shear modulus roughly twice that of Li could achieve stable electrodeposition. (39) In an earlier work, we extended this idea and showed that the criteria for the suppression of dendrite growth gets reversed for inorganic crystalline materials due to the difference in molar volume of Li+. A softer solid electrolyte is required for stability in this case. (40) It is worth highlighting that this requirement applies only for dendrite initiation regime, and other suppression approaches may be possible for the propagation regime. However, once initiated, dendrite growth is extremely hard to mitigate as pointed out by several studies. (41−43) Therefore, it is best to prevent dendrites from initiating to ensure smooth electrodeposition throughout cycling of the battery.
In recent years, high-throughput computational materials design has emerged as a major driver of discovery of novel materials for various applications. (44,45) It typically involves a combination of first-principles quantum-mechanical approaches and database construction and mining techniques. Combined with machine-learning methods that bypass the use of expensive quantum-mechanical calculations through the use of structural descriptors, (46−50) one can accelerate the high-throughput screening by several orders of magnitude. (51−53) Previous high-throughput screening studies of solid electrolytes have used ionic conductivity, stability, and electronic conductivity as screening criteria. (51,52) However, dendrite suppression capability of solid electrolytes is an additional requirement that needs to be assessed.
Here, we carry out a large-scale data-driven search for solid electrolytes that might be promising candidates for suppressing dendrite growth during the initiation phase with a Li metal anode. We use machine-learning techniques to train and predict the mechanical properties of inorganic solids which play a major role in stabilizing the interface. These properties are fed into the theoretical framework which uses the stability parameter (40,54) to quantify the dendrite initiation with Li metal anode. At a mechanically isotropic interface, the screening results predict the crucial role of surface tension in stabilizing the interface since most solid electrolytes are not intrinsically stabilized by the stresses generated at the interface. Hence, surface nanostructuring may be essential to prevent initiation of dendrites for isotropic interfaces. We rank the materials based on the amount of nanostructuring (surface roughness wavenumber) required for achieving a stable electrodeposition. We then performed a stability analysis of over 15 000 anisotropic interfaces between the Li metal and solid electrolyte using the Stroh formalism. This is essential to account for the highly anisotropic mechanical properties of Li (55) and texturing of electrodeposited Li at the interface. (56) A full anisotropic treatment of the interface reveals over 20 candidate interfaces that are predicted to suppress dendrite initiation. The materials obtained through screening are generally soft and with highly anisotropic mechanical properties. Since softer materials are generally faster ion conductors than stiffer materials due to availability of more volume per atom, (57) the screened candidates present an opportunity to obtain both desirable mechanical properties and fast ion conduction.

Results and Discussion

Click to copy section linkSection link copied!
We describe the procedure for screening solid electrolytes and discuss the material candidates obtained based on isotropic and anisotropic criteria separately. First, we briefly review the criteria for dendrite suppression at a metal–solid electrolyte interface to determine the desirable properties of solid electrolytes. We then discuss our machine-learning models used for predicting these properties. Finally, we perform material screening based on these properties and discuss the implications.

Isotropic Material Screening

In solid electrolytes, the mechanical properties at the interface provide an additional degree of freedom for tuning the stability of electrodeposition. Previously, we developed a generalized stability diagram for assessing the stability of electrodeposition at a metal–solid electrolyte interface for isotropic mechanical response. (40) In these studies, we used the stability parameter first proposed by Monroe and Newman (43) to characterize the growth/decay of dendrites with time. The sign of the stability parameter, denoted hereafter as χ, determines whether the electrodeposition is stable or unstable. A positive χ implies higher current density at the peaks and lower current density at the valley leading to growth of dendrites while a negative χ leads to stabilization or suppression of dendrites. The stability parameter is related to the change in the electrochemical potential of the electron Δμe– at a deformed interface z = f(x) between the metal anode and the electrolyte (Figure S1). It is convenient to compute properties of the interface in Fourier space with f(x) = ∫ dk[f1(k) cos(kx) + f2(k) sin(kx)] and then integrate over the surface roughness wavenumber k to obtain the overall behavior. The stability parameter can be calculated in a closed form at a given k. The change in the electrochemical potential at a given k is given by Δμe–(k) = χ(k)[f1(k) cos(kx) + f2(k) sin(kx)]. (54) This serves to define the stability parameter χ(k) as
(1)
Δμe– can be obtained by including the effect of mechanical stresses and surface tension on the electrochemical potential of the species at a deformed interface as (43)
(2)
From eq 2, Δμe– depends on k through the surface tension γ, curvature κ, the hydrostatic stress Δp, and deviatoric stress τ generated at the interface. e and s in the subscripts refer to the metal electrode (anode) and solid electrolyte, respectively; VM is the molar volume of the metal atom in the anode, v the ratio of molar volume of the metal ion in electrolyte VMz+ to the metal atom in the anode VM, z the valence of the metal, and en the normal to the interface pointing toward the electrolyte. The stability parameter consists of contributions from the surface tension and the stresses developed at the metal–electrolyte interface. For an isotropic metal anode with shear modulus Ge and Poisson’s ratio νe in contact with an isotropic electrolyte with shear modulus Gs and Poisson’s ratio νs, the stability parameter χ(k) can be computed exactly as (40)
(3)
Using the shear modulus, Poisson’s ratio, and molar volume ratio of a solid electrolyte, it is possible to calculate the stability parameter for its interface with Li metal anode and determine stability of electrodeposition. For a complete understanding of the interface growth and stability, it is necessary to determine the sign of stability parameter at all the Fourier components k. Fortunately, as we will see later, a negative stability parameter at a given k guarantees stability at all higher values.
The molar volume ratio v = VMz+/VM influences the range of shear moduli over which the electrodeposition is stable. VMz+ was calculated using the coordination number of Li in the crystal structure and mapping them to ionic radius using the values tabulated by Shannon. (58) The coordination number was calculated by generating polyhedra around a species through Voronoi analysis (59,60) as implemented in pymatgen. (61) A linear interpolation was used for computing ionic radius corresponding to coordination numbers not in the Shannon’s tabulated values. Predictions with VMz+ > VM (true for just one candidate in the screening) were ignored since those correspond to very high Li coordination number where Shannon’s tabulated values cannot be used. The partial molar volume of the metal in the electrolyte VMz+ can be measured directly in an experiment on the difference of potentials between a stressed and unstressed electrolyte as done by Pannikkat and Raj (62) and then using the relationship VMz+ = ∂μMz+/∂p where μMz+ is the electrochemical potential of the metal ion.
Since bulk and shear moduli are related to second derivatives of energy with respect to lattice constants at equilibrium, their calculation by first-principles requires fitting of the energy–strain relationship or the stress–strain relationship. Calculations on several deformed structures are required in order to get an accurate estimate of the fitting parameters. At each deformed state of the structure, the internal coordinates need to be relaxed to calculate the energy or the stress. The materials project database employed 24 relax calculations for a single material to compute the moduli. To perform a large-scale screening over all Li-containing compounds (over 12 000) for use as solid electrolytes, it is necessary to choose a technique that can predict the properties reasonably accurately and without the high computational cost of multiple first-principles simulations. Hence, we used the crystal graph convolutional neural network (CGCNN) framework (63) to predict the shear and bulk moduli of the crystalline solid electrolyte materials. At the core of the CGCNN is the multigraph representation of the crystal structure which encodes the atomic information and bonding interactions between atoms. The CGCNN builds a graph convolutional neural network directly on top of a multigraph that represents the crystal structure of the electrolytes, and predicts the elastic properties by extracting local structural features from the multigraph representation. Note that this method does not depend on any handcrafted geometric or topological features, and all the features are learned by the neural network automatically. This results in a model that is more general than the usual models relying on descriptors but also requires more data to train.
The training data for the mechanical properties required to compute χ through eq 3 was obtained from the materials project database. (64,65) The calculated values in the database are typically within 15% of the experimental values which is sometimes the uncertainty in experimental data. (65) The moduli have been calculated using density functional theory (DFT) with the Perdew, Burke, and Ernzerhof (PBE) generalized gradient approximation (GGA) for the exchange-correlation functional. (66) GGA-level predictions for 104 systems were within 15% of the experimental value for all but 16 systems for the bulk modulus and 15 systems for the shear modulus. (65) Out of the outliers, many had a discrepancy of less than 10 GPa. Experimentally, the shear and bulk moduli can be calculated using the elastic tensor obtained through inelastic neutron scattering or pulse–echo measurements. The experimental measurements typically have a high degree of variability depending on the experimental technique and conditions. We used 2041 crystal structures with shear and bulk moduli, 60% of the entire data set with elastic properties, to train our CGCNN model. We choose to minimize the mean squared errors between the log values of predicted and calculated elastic properties since we aim to minimize the relative prediction errors instead of absolute errors and avoid overweighing stiffer materials. This also enabled us to always obtain positive values of the shear and bulk moduli. We then performed a hyperparameter optimization on 20% validation data via grid search to select the optimum learning rate, weight decay, and number of convolution layers. The best performing hyperparameters are selected, and the resulting model is evaluated on the rest 20% test data. The CGCNN was implemented in PyTorch, (67) and the details of the architecture and optimized hyperparameters can be found in the Supporting Information and ref (63).
Considering the presence of uncertainty in training data set, we developed a framework for obtaining uncertainty estimates on the results. The uncertainty in the model predictions was obtained by generating an ensemble of 100 CGCNNs using a random 60% of the training data for each model. Using each model, we obtained predictions of the shear and bulk modulus. The ensemble of moduli was then used together with eq 3 to obtain an ensemble of stability parameters for each material. The spread in the distribution of the stability parameter was used to quantify the uncertainty. Figures S4 and S5 show the distribution of the shear modulus and stability parameter for the material Li5Sn2 as an example. Using the ensemble of stability parameters, we calculated the probability of stability Ps as the ratio of number of models that predict a negative stability parameter to the total number of models:
(4)
Here, N is the total number of models (100 in our calculations), and the indicator function 1{X} is equal to 1 if the condition X is true and 0 if it is false.
The performance of the CGCNN model was evaluated on 680 test data points. In Figure 1a, we show the comparison between the shear modulus predicted by our model and the DFT-calculated value obtained from the materials project database, and in Table 1, we show the root mean squared error (RMSE) for the shear and bulk moduli predicted by our model. The RMSE obtained using our model is comparable to previous work by de Jong et al. (49) However, it is worth noting that we evaluated our model on test data while de Jong et al. evaluated on the entire data set, indicating that our model does not overfit and has better generalization capability.

Figure 1

Figure 1. Parity plots comparing the elastic properties: (a) shear modulus G, and elastic constants (b) C11, (c) C12, and (d) C44 predicted by the machine-learning models to the DFT-calculated values. The shear modulus is predicted using CGCNN, and the elastic constants C11 and C44 are predicted using gradient boosting regression while C12 is predicted using kernel ridge regression. The parity plot for shear modulus is on 680 test data points while that for the elastic constants contains all available data (170 points) where each prediction is a cross-validated value.

High Resolution Image
Download MS PowerPoint Slide
Table 1. Comparison of RMSE in log(GPa) for Shear and Bulk Moduli
methodlog(G) RMSElog(K) RMSE
this work0.12680.1013
de Jong et al. (49)0.13780.0750
The shear modulus, Poisson’s ratio, and molar volume ratio v = VMz+/VM are the parameters determining the stability of electrodeposition at an interface where both materials are isotropic through eq 3. The role of surface tension in stabilizing electrodeposition is well established. (20,43,68) Since the contribution of the surface tension to the stability parameter increases as k2 while that of stress increases linearly with k, the surface tension starts dominating the stability parameter as k is increased. This is elucidated in Figure 2 through the contributions of the different terms to the total stability parameter for a material with G = 3.4 GPa and v = 0.1. The red line shows the fraction of contribution of surface tension to the overall stability parameter. All interfaces become stabilized as the value of k is increased beyond the critical surface roughness wavenumber. This motivates a distinction between two types of solid electrolytes—ones that are stabilized by the stress term alone and those that are stabilized by the surface tension beyond the critical value of k. For materials that are stabilized by stresses, the stability parameter remains negative for all values of surface roughness, and therefore, stability is guaranteed. However, for materials that have an overall destabilizing contribution due to stresses (hydrostatic + deviatoric), the stability parameter changes sign at an intermediate value of k since χ → −∞ as k → ∞. For such materials, the electrodeposition becomes stable at the critical surface wavenumber kcrit = 2π /λcrit (Figure 2). If the surface roughness wavenumber so obtained is possible to achieve by nanostructuring the interface, (20) the electrodeposition might be stabilized.

Figure 2

Figure 2. Contribution of hydrostatic stress, deviatoric stress, and surface tension to the stability parameter as a function of surface roughness wavenumber. The surface tension term starts dominating at high k and ultimately stabilizes the interface after k = kcrit. The contributions are plotted for a material with shear modulus ratio G/GLi = 1 and Poisson’s ratio ν = 0.33 which is not stable (χ > 0) at k = 108 m–1. The red line shows the fraction of surface tension contribution to the stability parameter obtained by dividing the absolute value of its contribution by the sum of absolute values of all components.

High Resolution Image
Download MS PowerPoint Slide
We calculated the stability parameter for 12 950 Li-containing compounds out of which the properties of ∼3400 were in training data, and those of the remaining were predicted using CGCNN. In Figure S3, we visualized the latent space representations of randomly selected 500 training and 500 predicted crystals in a two-dimensional plot using t-distributed stochastic neighbor embedding (t-SNE) algorithm. It is clear that training crystals cover the search space of predicted crystals, indicating the reliability of the prediction. The lower part of Figure S3 includes compounds that do not contain Li atoms, which helps improve the prediction even though they are not directly representative of the search space. The ensemble averaged stability parameter χ and the critical surface roughness λcrit for all materials are shown as a histogram in Figure 3. We found that none of the materials have a probability of stability over 5% at surface roughness wavenumber k = 108 m–1. (39) The absence of any materials that can suppress dendrites without assistance from surface tension becomes clear from the isotropic stability diagram shown in Figure 4. All materials have G/GLi ratio higher than the critical value required to stabilize electrodeposition. (40) The highest number of materials are found in the region where G/GLi ∼ 15 and v ∼ 0.1. The critical wavelength of surface roughening was used as the criteria for screening materials since a higher surface roughness is easier to achieve by nanostructuring.

Figure 3

Figure 3. Results of isotropic screening for 12 950 Li-containing compounds. Distribution of ensemble averaged (a) stability parameter for isotropic Li–solid electrolyte interfaces at k = 108 m–1 and (b) critical wavelength of surface roughness required for stability. None of the materials in the database can be stabilized without the aid of surface tension. The required critical surface roughness wavenumber depends on the contribution of the stress term in the stability parameter.

High Resolution Image
Download MS PowerPoint Slide

Figure 4

Figure 4. Isotropic stability diagram showing the position of all solid electrolytes involved in the screening. GLi is the shear modulus of Li = 3.4 GPa. The critical G/GLi line separating the stable and unstable regions depends weakly on the Poisson’s ratio, so the lines corresponding to νs = 0.33 and 0.5 are good indicators for assessment of stability. The darker regions indicate more number of materials in the region.

High Resolution Image
Download MS PowerPoint Slide
The candidate materials with highest critical wavelength of roughening λcrit are shown in Table 2 along with their stability parameter at different surface roughness wavenumbers and the corresponding probability of stability. While performing screening, we removed all materials which are electronically conducting, i.e., those which have a zero band gap according to the materials project database. However, we retained materials which were thermodynamically metastable (with energy above hull less than 0.1 eV) since many such solid electrolytes like Li10GeP2S12 (34) and Li7P3S11 (69) have been successfully synthesized. We find several candidate electrolytes with probability of stability Ps over 5% at surface roughness wavelength of 1 nm. It is worth noting that our screening identifies sulfide-, borohydride-, and iodide-based solid electrolytes, classes to which many of the current solid electrolytes belong. The uncertainty in stability parameter is much higher at high surface roughness wavenumber.
Table 2. Solid Electrolyte Screening Resultsa for Stable Electrodeposition with Li Metal Anode Together with Their materials project id (MP id) Ranked by Critical Wavelength of Surface Roughening λcrit Required to Stabilize Electrodeposition
   low khigh k 
formulaspace groupMP idχPsχPsλcrit (nm)
Li2WS4P4̅2mmp-8676950.62 –109.26 3.64
Li2WS4I4̅2mmp-7531951.75 –38.54 1.34
LiBH4P1̅mp-6759261.98 –40.13 1.32
LiAuI4P21/cmp-295202.7 ± 0.9016.1 ± 55.20.431.02 ± 0.40
LiGaI4P21/cmp-5679673.2 ± 1.1048.6 ± 67.00.280.85 ± 0.29
LiWCl6R3mp-5705123.2 ± 0.9051.3 ± 56.60.170.82 ± 0.27
Cs3LiI4P21/mmp-5692383.1 ± 0.7046.9 ± 43.40.150.80 ± 0.17
LiInI4P21/cmp-5410013.5 ± 1.0068.5 ± 62.80.120.74 ± 0.20
Cs2Li3I5C2mp-6083113.6 ± 0.9077.2 ± 59.00.050.71 ± 0.17
Ba19Na29Li35F4̅3mmp-5690254.2 ± 1.30101.9 ± 81.30.080.68 ± 0.19
Ba38Na58Li26NF4̅3mmp-5701854.2 ± 1.30104.5 ± 82.30.080.67 ± 0.20
Li2UI6P3̅1cmp-5708134.2 ± 1.40111.5 ± 86.80.110.66 ± 0.29
a

χ is the stability parameter in kJ/(mol nm) which needs to be negative for stability, and k = 2π/λ is the surface roughness wavenumber. Low k corresponds to k = 108 m–1 while high k corresponds to a wavelength λ = 2π/k = 1 nm. Only materials with probability of stability Ps > 0.05 at high k are shown. Uncertainties in χ and λcrit (standard deviation of their distributions) and Ps are only shown for materials whose properties were predicted using CGCNN and not for those whose properties were available in training data.

Anisotropic Material Screening

The isotropic assumption provides a good starting point for determining the stability of electrodeposition in terms of the mechanical and chemical properties of the solid electrolyte. However, this assumption does not take into account the huge anisotropy of Li metal resulting in an anisotropy index of 8.52. (55) This may also arise when the surface of the solids in contact are single crystalline or dominated by a particular crystallographic orientation as has been seen for Li metal. (56) Therefore, a full anisotropic treatment of the mechanics taking into account the elastic tensor and orientation of Li metal at the interface is essential. Previously, we also observed a large variation in the stability parameter, with the [010] orientation of Li and solid electrolyte at the interface being the most compliant. (54) Here, we analyze the stability of electrodeposition at an anisotropic interface involving crystalline solid electrolyte in contact with crystalline Li metal.
For this analysis, we calculate the stability parameter using the full elastic tensor of the electrode and the electrolyte. A knowledge of surface orientations at the interface is necessary to determine the elastic tensor. Specifically, we considered ⟨1 0 0⟩, ⟨1 1 0⟩, ⟨1 1 1⟩, and ⟨2 1 1⟩ crystallographic directions of Li normal to the interface, in contact with seven low-index facets of the solid electrolyte for the screening procedure. The last three crystallographic directions have been observed through cryo-electron microscopy as the dominant directions for Li dendrite growth in carbonate-based electrolytes. (70) Additionally, X-ray diffraction pole figure analysis has revealed that Li gets electrodeposited along certain preferential crystallographic orientations. (56) The crystallographic orientation provides one more parameter for tuning the mechanical properties at the interface. In an earlier work, we used the Stroh formalism (71,72) to calculate the stresses and deformations at the interface which were used to obtain the stability parameter χ. (54)
We developed a model different from CGCNN for predicting the full elastic tensor of each material since the size of training data is much smaller for the full elastic tensor. Considering the success of atomistic descriptors such as Li neighborhood, anionic framework, (57) bond ionicity, etc. in predicting Li-ion conduction, (52) we developed a regression model for predicting the elastic tensor of all Li-containing compounds using several of these relevant descriptors. A complete list of descriptors can be found in the Supporting Information. All descriptors used in the model are geometric or topological and can be obtained from the structure of the material in CIF format. (73) We further employed the crystal symmetries to reduce the number of parameters in the elastic tensor. This procedure reduces the number of constants in the elastic tensor to train from 21 to 3 in the cubic crystal class, for example, and, by construction, ensures the predicted elastic tensor obeys its crystal symmetry. (74) We trained separate models for each elastic constant, which were used to build the full elastic tensor of the material. We believe the differences in the uncertainty of the different elastic constants (75) result in different amounts of noise in the training data, thereby, justifying the need for separate models.
The training data for the elastic tensor was obtained from the materials project database (64,65) retrieved using pymatgen, (61) with the same level of theory as the isotropic case. We performed a data cleaning step in which all materials which were mechanically unstable (116 in number), i.e., with a nonpositive definite elastic tensor, were removed from training data. Since sufficient data was available only for the cubic crystal class (see Table S2 of the Supporting Information), we developed a regression model for predicting the elastic tensor of only cubic Li-containing compounds. However, our framework is very general and can accommodate more data as it is generated in training set as well as add models for other symmetries once the size of training data is sufficient in the materials project database. For other crystal symmetries besides cubic, we screened over materials with elastic tensor available from first-principles. Our screening process consisting of DFT-calculated data for all symmetries and predicted data for cubic symmetries that covers >15 000 Li metal–solid electrolyte interfaces.
The regression model was chosen on the basis of cross-validation performance (coefficient of determination) on the values of the elastic constants (in GPa). The models tested for performance were Lasso regression, elastic net, kernel ridge regression, Gaussian process regression, gradient boosting regression (76,77) used in an earlier work on predicting mechanical properties of zeolite frameworks, (53) AdaBoost regression, (78,79) support vector regression, (80) random forest regression, Bayesian ridge regression, (81) least angle regression, and automatic relevance determination. All hyperparameters were optimized using grid search with nested 3-fold cross-validation. The optimized hyperparameter values can be found in the Supporting Information. The scikit-learn package, (82) which has implementations of all models listed above, was used for training, hyperparameter optimization, and prediction.
Figure 1b–d shows the comparison of predicted and calculated values of elastic constants C11, C12, and C44. We obtain an overall R2 of 0.93, 0.92, and 0.92 and a cross-validation R2 of 0.60, 0.79, and 0.6 for the elastic constants C11, C12, and C44, respectively. A general idea of the effect of different atomistic features on the elastic constants can be obtained through the coefficients of a linear model fitted to the elastic constants. The dominant atomistic features affecting the elastic tensor based on the coefficients were mass density, volume per atom, sublattice electronegativity, and ratio of bond ionicities. The material stiffness increases with mass density and decreases with volume per atom. The stiffness also increases with ratio of bond ionicity of Li and sublattice and decreases with sublattice electronegativity.
While evaluating the models, the inherent uncertainty in DFT-calculated values due to exchange-correlation functional should be taken into account. (75,83) The uncertainty in DFT-calculated values generates noise in the training data. For example, DFT-calculated C12 generally has high uncertainty (75) which degrades the performance of boosting techniques. We believe the lower uncertainty in the training data for C11 and C44 makes boosting algorithms perform better. These algorithms are sensitive to the presence of outliers since a higher weight will be assigned to them. Further investigation is needed to quantify the effect of DFT uncertainty in training data on the machine-learning model to be used. Overall, our models have satisfactory performance to be used for the next step of prediction and screening. A two-dimensional t-SNE plot of all training and predicted feature data (Figure S6) also shows that out training data covers the feature space of the predicted crystals.
Our anisotropic screening procedure involves the determination of electrodeposition stability for 482 electrolyte materials with DFT-computed elastic tensor from the materials project database and 548 materials with cubic crystal structure whose elastic tensor was predicted using the regression model developed earlier. The total number of Li–solid electrolyte interfaces involved in the screening is over 15 000 (see also Table S2 in the Supporting Information). The best candidate interfaces obtained from the screening procedure are shown in Table 3. It is worth pointing out that all the screened candidates are stabilized by the stress term in the stability parameter and do not depend on surface tension for stability. We observe two major features in the materials obtained through screening: either the material is mechanically soft, i.e., with small eigenvalues of the elastic tensor, or the material has highly anisotropic mechanical properties. The mechanical softness of these materials is particularly attractive since it means the mechanical properties for dendrite suppression and ionic conductivity can be optimized simultaneously through methods like increasing volume per atom. (57) The high anisotropy results in certain weak directions along which the electrolyte may comply with the Li metal anode. This degree of freedom in crystallographic orientation is probably the reason why there are several screened candidates in the anisotropic case compared to the isotropic case. All screened candidates in Table 3 have a universal anisotropy index greater than 10, which is zero for an isotropic material. (84) The highest anisotropy index of 113.29 and 31.30 is found for LiOH and Li2WS4. The mechanically soft nature of these materials is reminiscent of density-driven stability (40) in the isotropic case for materials with v < 1.
Table 3. Screening Results for Anisotropic Interfaces (Top 20) for Stable Electrodeposition with Li Metal Anode with Their materials project id, Interface Normal, and Stability Parameter (Last Column Shows the Universal Anisotropy Index AU Which Is Zero for a Completely Isotropic Material) (84)
   interface normal  
formulaspace groupMP idLielectrolyteχ at k = 108 m–1 (kJ/(mol nm))AU
Li2WS4P4̅2mmp-867695[1 1 1][0 0 1]–1.9231.30
Li2WS4P4̅2mmp-867695[2 1 1][0 0 1]–1.8731.30
Li2WS4P4̅2mmp-867695[0 1 0][0 0 1]–1.6831.30
Li2WS4I4̅2mmp-753195[0 1 0][0 0 1]–1.2312.84
LiBH4P1̅mp-675926[0 1 0][0 1 0]–1.1213.65
Li2WS4I4̅2mmp-753195[1 1 1][1 0 1]–1.0012.84
LiOHP4/nmmmp-23856[0 1 0][0 0 1]–1.00113.29
Li2WS4I4̅2mmp-753195[1 1 1][0 0 1]–1.0012.84
LiOHP4/nmmmp-23856[2 1 1][0 0 1]–0.99113.29
LiOHP4/nmmmp-23856[1 1 1][0 0 1]–0.98113.29
Li2WS4I4̅2mmp-753195[2 1 1][0 0 1]–0.8912.84
Li2WS4I4̅2mmp-753195[0 1 0][1 0 1]–0.7912.84
LiBH4P1̅mp-675926[1 1 1][1 1 0]–0.7713.65
LiBH4P1̅mp-675926[1 1 1][0 1 0]–0.7513.65
Li2WS4I4̅2mmp-753195[0 1 0][0 1 1]–0.4913.84
LiBH4P1̅mp-675926[0 1 0][1 1 0]–0.4713.65
Li2WS4P4̅2mmp-867695[1 1 0][0 0 1]–0.4031.30
Li2WS4P4̅2mmp-867695[1 1 1][1 0 1]–0.2831.30
Li2WS4P4̅2mmp-867695[0 1 0][1 0 1]–0.1731.30
Li2WS4I4̅2mmp-753195[2 1 1][1 0 1]–0.0713.84

Other Considerations for Screened Electrolytes

Click to copy section linkSection link copied!
Although the literature on the candidates we obtained through screening is rather limited, we do find some similarities with solid electrolyte materials currently being explored. For example, Li borohydrides have been recently explored as fast ion conductors for solid state batteries. (85,86) Their anion substituted analogues also exhibit high conductivities and provide opportunities for tuning other desirable properties such as electrochemical and thermodynamic stability. (86−88) Li2WS4 phases are sulfides with structures similar to the tetragonal phase of LiFePO4. One of the candidates, LiOH, is often found as an electrochemically stable product in the solid electrolyte interphase formed at the anode. (89,90) The candidates we obtained through screening will only be useful if they satisfy several other requirements imposed by solid electrolytes. Here, we focus on the most important ones: ionic conductivity, electronic conductivity, and thermodynamic stability. We develop quantitative metrics for each of them and show them for the screened candidates in Table 4.
Table 4. Quantitative Metricsa for Other Important Requirements of Screened Solid Electrolytes for Their Use in Li-Ion Batteries: Ionic Conductivity, Electronic Conductivity, and Thermodynamic Stability
formulaspace groupMP idPionband gap (eV)energy above hull per atom (eV)
LiOHP4/nmmmp-238560.056.340.000
LiAuI4P21/cmp-295200.941.920.000
LiGaI4P21/cmp-5679670.184.330.000
LiBH4P1̅mp-6759260.278.570.071
Li2WS4I4̅2mmp-7531950.153.610.032
Li2WS4P4̅2mmp-8676950.233.520.037
Cs3LiI4P21/mmp-5692380.016.070.018
LiInI4P21/cmp-5410010.133.960.000
Cs2Li3I5C2mp-6083110.336.580.000
Ba19Na29Li35F4̅3mmp-5690250.000.940.000
Ba38Na58Li26NF4̅3mmp-5701851.000.960.009
Li2UI6P3̅1cmp-5708130.261.170.000
a

Ionic conductivity is quantified through Pion, the probability of superionic conduction; electronic conductivity through the band gap; and thermodynamic stability through energy per atom above the convex hull.

Ionic Conductivity

We obtained the probability of superionic conduction by using the logistic regression framework proposed by Sendek et al. (52) which relates the atomistic features to conductivity. The probability can be obtained using the features 6, 7, 9, 12, and 14 from the feature list in the Supporting Information. These probabilities are based on atomic structure of the bulk. Of the screened candidates, LiAuI4 and Ba38Na58Li26N are predicted to be superionic conductors. Further enhancement in conductivities may be achieved through doping, defects, and disorder. The high-temperature hexagonal phase of LiBH4 has a Li-ion conductivity as high as 10 mS/cm. (91) The screened triclinic P1̅ phase of LiBH4 is disordered and is expected to have higher Li ionic conductivity than that obtained for bulk atomic structure. It will be worth investigating the possibility of tuning its ionic conduction and mechanical properties through anion substitution similar to its hexagonal analogue which undergoes order of magnitude improvements in ionic conductivity. (87,88) LiOH is known to undergo a solid state transition at ∼415 °C accompanied by a large increase in ionic conductivity. (92) We also expect the high anisotropy in mechanical properties of some electrolytes to affect the activation energy landscape for Li-ion conduction as well, possibly leading to anisotropic conductivities.

Thermodynamic Stability

While screening, we only removed solids with energy greater than 0.1 eV/atom above hull. Besides LiBH4 and Li2WS4, all screened candidates are thermodynamically stable; i.e., energy above the convex hull is 0 eV, or the energy above hull per atom is within thermal fluctuations at room temperature. The two phases of Li2WS4 have energies 31 and 36 meV per atom above hull. The hexagonal phase of LiBH4 has been explored previously as a solid electrolyte with promising performance. (91,93,94) This phase is thermodynamically unstable at room temperature. However, doping with Li halides improved the stability of the hexagonal phase even below room temperature. (93) It is possible that a similar technique might help stabilize the screened triclinic P1̅ phase obtained through our screening as well.

Electronic Conductivity

All the screened solid electrolytes in the list are electronic insulators since we removed those with a zero GGA band gap. Since GGA DFT generally underestimates band gaps, we used an empirical scheme to correct for the band gap (95) (see the Supporting Information). All candidates apart from Ba19Na29Li35 and Ba38Na58Li26N have corrected band gaps greater than 1 eV.
We found several candidates that could enable dendrite suppression through the anisotropic criteria. It is worth highlighting that grain misorientation needs to be avoided for these materials. Textured growth of Li film on Li metal anode has been observed for liquid electrolytes. (56) A similar opportunity for textured growth of Li metal is present with solid electrolytes; however, it would require precise engineering of film growth at the interphase which requires further investigation.
While the candidate list identified is small, we strongly believe that there is a lot of room for further search for candidates. In particular, we find that anisotropy plays a crucial role in determining the stability, and thus, there may be additional noncubic Li-containing compounds that could suppress dendrites at certain crystallographic orientations. Further, the degree of disorder is another important factor, and many glassy or amorphous materials are known to be good solid electrolytes. These avenues will be the subject of our future investigations.

Conclusions

Click to copy section linkSection link copied!
We applied criteria for stable electrodeposition together with machine-learning techniques to computationally screen solid electrolytes for suppressing Li dendrite growth. The machine-learning techniques accelerate the process of screening by predicting the properties of solid electrolytes through the identification of structure–property relationships. We train a graph convolutional neural network on shear and bulk moduli. We employ gradient boosting regression and kernel ridge regression for training the elastic constants of cubic materials. The choice of machine-learning models used was rationalized by the model’s ability to handle noise in the training data. Our approach is readily applicable for screening materials for other properties of interest and can easily accommodate more data as it is generated.
Our predictive models enabled us to screen 12 950 solids using isotropic stability criteria and over 15 000 interfaces using anisotropic stability criteria of electrodeposition on the Li metal anode. In the isotropic case, we found no materials that could be stabilized solely by the stresses generated at the interface; however, a surface tension-mediated stabilization was found to be possible at high surface roughness wavenumbers. In the anisotropic case, the additional degree of freedom related to crystallographic orientation of the solid electrolyte at the interface enabled us to find over 20 interfaces with six solid electrolytes that are predicted to be stable to dendrite initiation. We identify some common features like anisotropy and mechanical softness present in the screened candidates based on which one can simultaneously optimize properties required for dendrite suppression as well as fast ion conduction. We believe that the use of techniques like doping and defect generation will be crucial to ensure simultaneous satisfaction of other solid electrolyte requirements for screened candidates.

Supporting Information

Click to copy section linkSection link copied!

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acscentsci.8b00229.

  • Details of machine-learning models and additional figures including schematics and visualizations, shear modulus values, and stability parameter values (PDF)

  • Anisotropic stability parameter for training data (XLSX)

  • Shear modulus of 60 648 compounds predicted using CGCNN (XLSX)

  • Anisotropic stability parameter for predicted data (XLSX)

  • Bulk modulus of 60 648 compounds predicted using CGCNN (XLSX)

Terms & Conditions

Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

Author Information

Click to copy section linkSection link copied!
  • Corresponding Author
    • Venkatasubramanian Viswanathan - Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States;  Department of Physics, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States;  Orcidhttp://orcid.org/0000-0003-1060-5495;  Email: [email protected]
  • Authors
    • Zeeshan Ahmad - Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States;  Orcidhttp://orcid.org/0000-0001-9758-8952
    • Tian Xie - Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States;  Orcidhttp://orcid.org/0000-0002-0987-4666
    • Chinmay Maheshwari - Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
    • Jeffrey C. Grossman - Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

Click to copy section linkSection link copied!

Z.A. thanks A. Sendek for sharing data on features of Li-containing compounds, and S. Shekhar, C. Hwu, L. Kara, J. Montoya, and K. Thomas-Alyea for helpful discussions. Z.A. acknowledges support from the Advanced Research Projects Agency—Energy (ARPA-E) Integration and Optimization of Novel Ion Conducting Solids (IONICS) program under Grant DE-AR0000774. V.V. gratefully acknowledges support from the U.S. Department of Energy, Energy Efficiency and Renewable Energy Vehicle Technologies Office under Award DE-EE0007810. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), (96) which is supported by National Science Foundation Grant OCI-1053575. Specifically, it used Grant MSS170010P on the Bridges system, (97) which is supported by NSF Award ACI-1445606, at the Pittsburgh Supercomputing Center (PSC).

References

Click to copy section linkSection link copied!

This article references 97 other publications.

  1. 1
    Sripad, S.; Viswanathan, V. Evaluation of Current, Future, and Beyond Li-Ion Batteries for the Electrification of Light Commercial Vehicles: Challenges and Opportunities. J. Electrochem. Soc. 2017, 164, E3635– E3646,  DOI: 10.1149/2.0671711jes
    Google Scholar
    1
    Evaluation of Current, Future, and Beyond Li-Ion Batteries for the Electrification of Light Commercial Vehicles: Challenges and Opportunities
    Sripad, Shashank; Viswanathan, Venkatasubramanian
    Journal of the Electrochemical Society (2017), 164 (11), E3635-E3646CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
    While the electrification of the transportation sector is underway with a consistently increasing market share, segments like light com. vehicles (LCVs) have not seen any significant market penetration. The major barriers are primarily in the shortcomings in specific energy, power, and cost assocd. with Li-ion batteries. With an initiative from several automakers to electrify other segments, there is a need to critically examine the performance requirements and to understand the factors governing such a transition in the near-term and long-term. In this study, a systematic methodol. framework is developed to analyze the performance demands for electrification through an approach that couples considerations for battery chemistries, load profiles based on a set of vehicle-specific drive cycles, and finally applying these loads to a battery pack that solves a 1-D thermally coupled battery model within the AutoLion-ST framework. Using this framework, the performance is analyzed of a fully elec. LCV over its lifetime under various driving conditions and explore the trade-offs between battery metrics and vehicle design parameters. It was found that in order to enable a driving range of over 400-mi for LCVs at a realistic battery pack wt., specific energies of over 400 Wh/kg at the cell-level and 200 Wh/kg at the pack-level needs to be achieved. A crucial factor that could bring down both the energy requirements and cost is through a vehicle re-design that lowers the drag coeff. to about 0.3.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsFClt77J&md5=f03859a781a14cdf43ac4f07bc79fd7d
  2. 2
    Sripad, S.; Viswanathan, V. Performance Metrics Required of Next-Generation Batteries to Make a Practical Electric Semi Truck. ACS Energy Lett. 2017, 2, 1669– 1673,  DOI: 10.1021/acsenergylett.7b00432
    Google Scholar
    2
    Performance Metrics Required of Next-Generation Batteries to Make a Practical Electric Semi Truck
    Sripad, Shashank; Viswanathan, Venkatasubramanian
    ACS Energy Letters (2017), 2 (7), 1669-1673CODEN: AELCCP; ISSN:2380-8195. (American Chemical Society)
    There is enormous interest around elec. Semi Truck and in order to understand the practical limitations imposed by the battery pack, a rigorous and thorough anal. considering battery metrics as well as vehicle design parameters is required. In this study, we find that an elec. semi truck, such as the envisioned Tesla Semi, would be limited to a driving range of 300 mi, a small payload capacity, and a prohibitively high cost. Enabling a longer driving range of 600 mi needed by the current stds. of the trucking industry might require a transition to beyond Li-ion batteries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVGrs7fL&md5=9ec6ad16dc50a0fd2d41a74a950d9fa6
  3. 3
    Moore, M. D. Misconceptions of Electric Aircraft and their Emerging Aviation Markets; AIAA SciTech Forum: American Institute of Aeronautics and Astronautics, 2014.
    Google Scholar
    There is no corresponding record for this reference.
  4. 4
    Cheng, X.-B.; Zhang, R.; Zhao, C.-Z.; Zhang, Q. Toward Safe Lithium Metal Anode in Rechargeable Batteries: A Review. Chem. Rev. 2017, 117, 10403– 10473,  DOI: 10.1021/acs.chemrev.7b00115
    Google Scholar
    4
    Toward Safe Lithium Metal Anode in Rechargeable Batteries: A Review
    Cheng, Xin-Bing; Zhang, Rui; Zhao, Chen-Zi; Zhang, Qiang
    Chemical Reviews (Washington, DC, United States) (2017), 117 (15), 10403-10473CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
    A review is presented. The lithium metal battery is strongly considered to be one of the most promising candidates for high-energy-d. energy storage devices in our modern and technol.-based society. However, uncontrollable lithium dendrite growth induces poor cycling efficiency and severe safety concerns, dragging lithium metal batteries out of practical applications. This review presents a comprehensive overview of the lithium metal anode and its dendritic lithium growth. First, the working principles and tech. challenges of a lithium metal anode are underscored. Specific attention is paid to the mechanistic understandings and quant. models for solid electrolyte interphase (SEI) formation, lithium dendrite nucleation, and growth. On the basis of previous theor. understanding and anal., recently proposed strategies to suppress dendrite growth of lithium metal anode and some other metal anodes are reviewed. A section dedicated to the potential of full-cell lithium metal batteries for practical applications is included. A general conclusion and a perspective on the current limitations and recommended future research directions of lithium metal batteries are presented. The review concludes with an attempt at summarizing the theor. and exptl. achievements in lithium metal anodes and endeavors to realize the practical applications of lithium metal batteries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXht1eku7bK&md5=f83e2bc869af2a2d65226611e96c8227
  5. 5
    Lin, D.; Liu, Y.; Cui, Y. Reviving the lithium metal anode for high-energy batteries. Nat. Nanotechnol. 2017, 12, 194– 206,  DOI: 10.1038/nnano.2017.16
    Google Scholar
    5
    Reviving the lithium metal anode for high-energy batteries
    Lin, Dingchang; Liu, Yayuan; Cui, Yi
    Nature Nanotechnology (2017), 12 (3), 194-206CODEN: NNAABX; ISSN:1748-3387. (Nature Publishing Group)
    A review is given. Li-ion batteries have had a profound impact on our daily life, but inherent limitations make it difficult for Li-ion chemistries to meet the growing demands for portable electronics, elec. vehicles and grid-scale energy storage. Therefore, chemistries beyond Li-ion are currently being investigated and need to be made viable for com. applications. The use of metallic Li is one of the most favored choices for next-generation Li batteries, esp. Li-S and Li-air systems. After falling into oblivion for several decades because of safety concerns, metallic Li is now ready for a revival, thanks to the development of investigative tools and nanotechnol.-based solns. Here, we 1st summarize the current understanding on Li anodes, then highlight the recent key progress in materials design and advanced characterization techniques, and finally discuss the opportunities and possible directions for future development of Li anodes in applications.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXmtVyitr4%253D&md5=07d29fc449ccc1f6c941b4c7692a8639
  6. 6
    Christensen, J.; Albertus, P.; Sanchez-Carrera, R. S.; Lohmann, T.; Kozinsky, B.; Liedtke, R.; Ahmed, J.; Kojic, A. A Critical Review of Li/Air Batteries. J. Electrochem. Soc. 2011, 159, R1– R30,  DOI: 10.1149/2.086202jes
    Google Scholar
    There is no corresponding record for this reference.
  7. 7
    Xu, W.; Wang, J.; Ding, F.; Chen, X.; Nasybulin, E.; Zhang, Y.; Zhang, J.-G. Lithium metal anodes for rechargeable batteries. Energy Environ. Sci. 2014, 7, 513– 537,  DOI: 10.1039/C3EE40795K
    Google Scholar
    7
    Lithium metal anodes for rechargeable batteries
    Xu, Wu; Wang, Jiulin; Ding, Fei; Chen, Xilin; Nasybulin, Eduard; Zhang, Yaohui; Zhang, Ji-Guang
    Energy & Environmental Science (2014), 7 (2), 513-537CODEN: EESNBY; ISSN:1754-5706. (Royal Society of Chemistry)
    A review. Lithium (Li) metal is an ideal anode material for rechargeable batteries due to its extremely high theor. specific capacity (3860 mA h g-1), low d. (0.59 g cm-3) and the lowest neg. electrochem. potential (-3.040 V vs. the std. hydrogen electrode). Unfortunately, uncontrollable dendritic Li growth and limited Coulombic efficiency during Li deposition/stripping inherent in these batteries have prevented their practical applications over the past 40 years. With the emergence of post-Li-ion batteries, safe and efficient operation of Li metal anodes has become an enabling technol. which may det. the fate of several promising candidates for the next generation energy storage systems, including rechargeable Li-air batteries, Li-S batteries, and Li metal batteries which utilize intercalation compds. as cathodes. In this paper, various factors that affect the morphol. and Coulombic efficiency of Li metal anodes have been analyzed. Technologies utilized to characterize the morphol. of Li deposition and the results obtained by modeling of Li dendrite growth have also been reviewed. Finally, recent development and urgent need in this field are discussed.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsFaktL8%253D&md5=3813f919eb7ca4218cfb562f54c9382a
  8. 8
    Tikekar, M. D.; Choudhury, S.; Tu, Z.; Archer, L. A. Design principles for electrolytes and interfaces for stable lithium-metal batteries. Nat. Energy 2016, 1, 16114,  DOI: 10.1038/nenergy.2016.114
    Google Scholar
    8
    Design principles for electrolytes and interfaces for stable lithium-metal batteries
    Tikekar, Mukul D.; Choudhury, Snehashis; Tu, Zhengyuan; Archer, Lynden A.
    Nature Energy (2016), 1 (9), 16114CODEN: NEANFD; ISSN:2058-7546. (Nature Publishing Group)
    A review. The future of electrochem. energy storage hinges on the advancement of science and technol. that enables rechargeable batteries that utilize reactive metals as anodes. With specific capacity more than ten times that of the LiC6 anode used in present-day lithium-ion batteries, cells based on Li-metal anodes are of particular interest. Effective strategies for stabilizing the anode in such cells are now understood to be a requirement for progress on exceptional storage technologies, including Li-S and Li-O2 batteries. Multiple challenges-parasitic reactions of Li-metal with liq. electrolytes, unstable and dendritic electrodeposition, and dendrite-induced short circuits-derailed early efforts to commercialize such lithium-metal batteries. Here we consider approaches for rationally designing electrolytes and Li-metal/electrolyte interfaces for stable, dendrite-free operation of lithium-metal batteries. On the basis of fundamental understanding of the failure modes of reactive metal anodes, we discuss the key variables that govern the stability of electrodeposition at the Li anode and propose a universal framework for designing stable electrolytes and interfaces for lithium-metal batteries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVersL8%253D&md5=3627028eaacfbf47fd2a8691cfddcef1
  9. 9
    Aurbach, D.; Zinigrad, E.; Teller, H.; Dan, P. Factors Which Limit the Cycle Life of Rechargeable Lithium (Metal) Batteries. J. Electrochem. Soc. 2000, 147, 1274– 1279,  DOI: 10.1149/1.1393349
    Google Scholar
    9
    Factors which limit the cycle life of rechargeable lithium (metal) batteries
    Aurbach, D.; Zinigrad, E.; Teller, H.; Dan, P.
    Journal of the Electrochemical Society (2000), 147 (4), 1274-1279CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
    Failure mechanisms due to high charging rates of rechargeable lithium batteries comprised of Li anodes, Li0.3MnO2 cathodes (tunneled structure), and electrolyte solns. based on the combination of 1,3-dioxolane, LiAsF6, and tributylamine (antipolymn. stabilizer) were explored with the aid of postmortem anal. It was found that at high charging rates, lithium deposition produces small grains, which are too reactive toward the electrolyte soln., in spite of the excellent passivation of lithium in this soln. In practical batteries such as AA cells with spirally wound configurations, the amt. of soln. is relatively small, and the soln. is spread throughout the battery in a thin layer. Therefore, upon cycling, the Li-soln. reactions deplete the amt. of the soln. below a crit. value, so that only part of the active materials continues to function. This leads to a pronounced increase in the internal resistance of these batteries, which fail as a result of their high impedance and the decrease in the effective working electrodes area. Another failure mechanism relates to the extremely high charge-discharge current densities developed as the active electrode area decreases. These high currents, developed after prolonged cycling, lead to the formation of dendrites that short-circuit the battery, thus terminating its life.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXisVygtLo%253D&md5=9c7dc9d384db266edbddf0a3ae3d6f0c
  10. 10
    Aurbach, D.; Zinigrad, E.; Cohen, Y.; Teller, H. A short review of failure mechanisms of lithium metal and lithiated graphite anodes in liquid electrolyte solutions. Solid State Ionics 2002, 148, 405– 416,  DOI: 10.1016/S0167-2738(02)00080-2
    Google Scholar
    10
    A short review of failure mechanisms of lithium metal and lithiated graphite anodes in liquid electrolyte solutions
    Aurbach, Doron; Zinigrad, Ella; Cohen, Yaron; Teller, Hanan
    Solid State Ionics (2002), 148 (3,4), 405-416CODEN: SSIOD3; ISSN:0167-2738. (Elsevier Science B.V.)
    A review. Li electrodes in any relevant electrolyte soln. (i.e., polar aprotic) are covered by surface films of a very complicated structure. It was found that even in cases where the surface films formed on lithium contain elastomers, or where the lithium metal reactivity is reduced by doping with elements such as N, As, Al, Mg, Ca, etc., it is impossible to achieve sufficient passivation with lithium electrodes and liq. solns. Passivation is considerably worsened when Li electrodes are operated at high rates (esp. at high charging, Li deposition rates). Thus, there is no way that rechargeable Li batteries can compete with Li-ion batteries in any application that requires high charging rates (e.g., in powering portable electronic devices). The electrochem. behavior of lithiated graphite electrodes also depends on passivation phenomena. The surface films formed on lithiated graphite are similar to those formed on Li metal in the same solns. The vol. changes of graphite electrodes during Li insertion-deinsertion are small enough to enable their reasonable passivation in a variety of electrolyte solns. A crit. factor that dets. the stability of graphite electrodes is their morphol. It was found that the shape of graphite particles plays a key role in their application as active mass in anodes for Li-ion batteries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38XktVWgurY%253D&md5=24417ffbcd25ad6ffaba30ab7d00e3d4
  11. 11
    Steiger, J.; Kramer, D.; Mönig, R. Mechanisms of dendritic growth investigated by in situ light microscopy during electrodeposition and dissolution of lithium. J. Power Sources 2014, 261, 112– 119,  DOI: 10.1016/j.jpowsour.2014.03.029
    Google Scholar
    11
    Mechanisms of dendritic growth investigated by in situ light microscopy during electrodeposition and dissolution of lithium
    Steiger, Jens; Kramer, Dominik; Moenig, Reiner
    Journal of Power Sources (2014), 261 (), 112-119CODEN: JPSODZ; ISSN:0378-7753. (Elsevier B.V.)
    Batteries with metallic lithium anodes offer improved volumetric and gravimetric energy densities; therefore, future batteries including the promising lithium-sulfur and lithium-air systems would benefit from them. The electrodeposition of lithium metal - which is an unwanted incident in lithium ion systems - often results in fine filaments or moss, called dendritic lithium, which leads to strong capacity fading and the danger of internal short circuiting. To study the mechanisms of dendritic growth and the behavior during lithium dissoln., lithium deposits have been obsd. in situ in 1 M LiPF6 in EC:DMC by light microscopy. The high resoln. optical microscopy provided information on the growth and electrodissoln. of single lithium filaments. The growth areas could be identified in detail: The lithium wires can grow either from the substrate-lithium interface, at kinks or in a region at or close to the tip. Based on these observations, we suggest a growth model for lithium filaments predicated on defect-based insertion of lithium at the aforementioned locations. This type of growth is not compatible with previous models of dendritic growth, for example, it is hardly influenced by elec. fields at the tip and does not depend on the direction of the elec. field.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXnsl2murc%253D&md5=2db8d2782ea70c21bf11b86d6c3618fd
  12. 12
    Albertus, P.; Babinec, S.; Litzelman, S.; Newman, A. Status and challenges in enabling the lithium metal electrode for high-energy and low-cost rechargeable batteries. Nat. Energy 2018, 3, 16– 21,  DOI: 10.1038/s41560-017-0047-2
    Google Scholar
    12
    Status and challenges in enabling the lithium metal electrode for high-energy and low-cost rechargeable batteries
    Albertus, Paul; Babinec, Susan; Litzelman, Scott; Newman, Aron
    Nature Energy (2018), 3 (1), 16-21CODEN: NEANFD; ISSN:2058-7546. (Nature Research)
    Enabling the reversible lithium metal electrode is essential for surpassing the energy content of today's lithium-ion cells. Although lithium metal cells for niche applications have been developed already, efforts are underway to create rechargeable lithium metal batteries that can significantly advance vehicle electrification and grid energy storage. In this Perspective, we focus on three tasks to guide and further advance the reversible lithium metal electrode. First, we summarize the state of research and com. efforts in terms of four key performance parameters, and identify addnl. performance parameters of interest. We then advocate for the use of limited lithium (≤30 μm) to ensure early identification of tech. challenges assocd. with stable and dendrite-free cycling and a more rapid transition to com. relevant designs. Finally, we provide a cost target and outline material costs and manufg. methods that could allow lithium metal cells to reach 100 US$ kWh-1.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXitVehurY%253D&md5=569e6865fb3a225323353f9e9f39fddb
  13. 13
    Aurbach, D.; Markovsky, B.; Shechter, A.; Ein-Eli, Y.; Cohen, H. A Comparative Study of Synthetic Graphite and Li Electrodes in Electrolyte Solutions Based on Ethylene Carbonate-Dimethyl Carbonate Mixtures. J. Electrochem. Soc. 1996, 143, 3809– 3820,  DOI: 10.1149/1.1837300
    Google Scholar
    13
    A comparative study of synthetic graphite and Li electrodes in electrolyte solutions based on ethylene carbonate-dimethyl carbonate mixtures
    Aurbach, D.; Markovsky, B.; Shechter, A.; Ein-Eli, Y.; Cohen, H.
    Journal of the Electrochemical Society (1996), 143 (12), 3809-3820CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
    This work entails a comparative study of both Li and synthetic graphite electrodes in electrolyte solns. based on ethylene and di-Me carbonates (EC-DMC) and the impact of the salt used [LiAsF6, LiClO4, LiPF6, LiBF4, and LiN(SO2CF3)2]. The presence of some additives in solns. (e.g., Li2CO3, CO2, tributylamine) and the effect of the particle size of the carbon on the behavior of the electrode were studied. The correlation between the surface chem., the morphol., and the performance of Li and graphite electrodes was explored using surface sensitive FTIR and x-ray and photoelectron spectroscopies, impedance spectroscopy, x-ray diffraction and SEM in conjunction with std. electrochem. techniques. Synthetic graphite anodes could be cycled (Li intercalation-deintercalation) hundreds of times at a capacity close to the optimal (x → 1 in LixC6) in EC-DMC solns. due to the formation of highly stable and passivating surface films in which EC redn. products such as (CH2OCO2Li)2 are the major constituents. The cycling efficiency of Li metal anodes in these solns., however, is lower than that obtained in ethereal solns. and seems to be too low for Li-metal liq. electrolyte, rechargeable battery application. The connection between the soln. compn. and the electrode's performance is discussed.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2sXhsFehsg%253D%253D&md5=02e52a156683cedd7ca6901f81eb7313
  14. 14
    Hirai, T.; Yoshimatsu, I.; Yamaki, J. Effect of Additives on Lithium Cycling Efficiency. J. Electrochem. Soc. 1994, 141, 2300– 2305,  DOI: 10.1149/1.2055116
    Google Scholar
    14
    Effect of additives on lithium cycling efficiency
    Hirai, Toshiro; Yoshimatsu, Isamu; Yamaki, Jun-ichi
    Journal of the Electrochemical Society (1994), 141 (9), 2300-5CODEN: JESOAN; ISSN:0013-4651.
    Li cycling efficiency was evaluated for LiAsF6-ethylene carbonate/2-methyltetrahydrofuran mixed-solvent electrolyte (LiAsF6-EC/2MeTHF) contg. additives of tetraalkylammonium chlorides with a long n-alkyl chain and three Me groups. The tetraalkylammonium chloride with n-alkyl group longer than n-C12H25 increased Li cycling efficiency. Cetyltrimethylammonium chloride (CTAC) produced the best improvement in Li cycling efficiency. A figure of merit (FOM) of Li for 0.01M CTAC was 46, which was 1.5 times the FOM for the corresponding additive-free electrolyte. The LiAsF6-EC/2MeTHF with CTAC showed an increase in FOM with stack pressure, but the effect was less than that for the additive-free LiAsF6-EC/2MeTHF. SEM observation showed that the addn. of CTAC decreased the needle-like Li deposition and increased particulate Li deposition. This deposition morphol. may be the main cause of the increase in FOM. The additive had no effect on rate capability for cell cycling at 3 mA/cm2 discharge and 1 mA/cm2 charge.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2cXmtlCls7o%253D&md5=9125cb1237f464f238f6507a83b742ac
  15. 15
    Ding, F.; Xu, W.; Graff, G. L.; Zhang, J.; Sushko, M. L.; Chen, X.; Shao, Y.; Engelhard, M. H.; Nie, Z.; Xiao, J.; Liu, X.; Sushko, P. V.; Liu, J.; Zhang, J.-G. Dendrite-Free Lithium Deposition via Self-Healing Electrostatic Shield Mechanism. J. Am. Chem. Soc. 2013, 135, 4450– 4456,  DOI: 10.1021/ja312241y
    Google Scholar
    15
    Dendrite-Free Lithium Deposition via Self-Healing Electrostatic Shield Mechanism
    Ding, Fei; Xu, Wu; Graff, Gordon L.; Zhang, Jian; Sushko, Maria L.; Chen, Xilin; Shao, Yuyan; Engelhard, Mark H.; Nie, Zimin; Xiao, Jie; Liu, Xingjiang; Sushko, Peter V.; Liu, Jun; Zhang, Ji-Guang
    Journal of the American Chemical Society (2013), 135 (11), 4450-4456CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
    Rechargeable Li metal batteries are of great importance. Unfortunately, uncontrollable dendritic Li growth inherent in these batteries (upon repeated charge/discharge cycling) has prevented their practical application over the past 40 years. The authors show a novel mechanism that can fundamentally alter dendrite formation. At low concns., selected cations (such as Cs or Rb ions) exhibit an effective redn. potential below the std. redn. potential of Li ions. During Li deposition, these additive cations form a pos. charged electrostatic shield around the initial growth tip of the protuberances without redn. and deposition of the additives. This forces further deposition of Li to adjacent regions of the anode and eliminates dendrite formation in Li metal batteries. This strategy may also prevent dendrite growth in Li-ion batteries as well as other metal batteries and transform the surface uniformity of coatings deposited in many general electrodeposition processes.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXjtlWgsLs%253D&md5=be89c1fc31e4b3293c7f0fb4e6b55407
  16. 16
    Qian, J.; Henderson, W. A.; Xu, W.; Bhattacharya, P.; Engelhard, M.; Borodin, O.; Zhang, J.-G. High rate and stable cycling of lithium metal anode. Nat. Commun. 2015, 6, 6362,  DOI: 10.1038/ncomms7362
    Google Scholar
    16
    High rate and stable cycling of lithium metal anode
    Qian, Jiangfeng; Henderson, Wesley A.; Xu, Wu; Bhattacharya, Priyanka; Engelhard, Mark; Borodin, Oleg; Zhang, Ji-Guang
    Nature Communications (2015), 6 (), 6362CODEN: NCAOBW; ISSN:2041-1723. (Nature Publishing Group)
    Lithium metal is an ideal battery anode. However, dendrite growth and limited Coulombic efficiency during cycling have prevented its practical application in rechargeable batteries. Herein, we report that the use of highly concd. electrolytes composed of ether solvents and the lithium bis(fluorosulfonyl)imide salt enables the high-rate cycling of a lithium metal anode at high Coulombic efficiency (up to 99.1%) without dendrite growth. With 4 M lithium bis(fluorosulfonyl)imide in 1,2-dimethoxyethane as the electrolyte, a lithium|lithium cell can be cycled at 10 mA cm-2 for more than 6,000 cycles, and a copper|lithium cell can be cycled at 4 mA cm-2 for more than 1,000 cycles with an av. Coulombic efficiency of 98.4%. These excellent performances can be attributed to the increased solvent coordination and increased availability of lithium ion concn. in the electrolyte. Further development of this electrolyte may enable practical applications for lithium metal anode in rechargeable batteries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtF2itrrI&md5=9bde3388626c7f3d31618a2d934a90cf
  17. 17
    Suo, L.; Hu, Y.-S.; Li, H.; Armand, M.; Chen, L. A new class of Solvent-in-Salt electrolyte for high-energy rechargeable metallic lithium batteries. Nat. Commun. 2013, 4, 1481,  DOI: 10.1038/ncomms2513
    Google Scholar
    17
    A new class of Solvent-in-Salt electrolyte for high-energy rechargeable metallic lithium batteries
    Suo Liumin; Hu Yong-Sheng; Li Hong; Armand Michel; Chen Liquan
    Nature communications (2013), 4 (), 1481 ISSN:.
    Liquid electrolyte plays a key role in commercial lithium-ion batteries to allow conduction of lithium-ion between cathode and anode. Traditionally, taking into account the ionic conductivity, viscosity and dissolubility of lithium salt, the salt concentration in liquid electrolytes is typically less than 1.2 mol l(-1). Here we show a new class of 'Solvent-in-Salt' electrolyte with ultrahigh salt concentration and high lithium-ion transference number (0.73), in which salt holds a dominant position in the lithium-ion transport system. It remarkably enhances cyclic and safety performance of next-generation high-energy rechargeable lithium batteries via an effective suppression of lithium dendrite growth and shape change in the metallic lithium anode. Moreover, when used in lithium-sulphur battery, the advantage of this electrolyte is further demonstrated that lithium polysulphide dissolution is inhibited, thus overcoming one of today's most challenging technological hurdles, the 'polysulphide shuttle phenomenon'. Consequently, a coulombic efficiency nearing 100% and long cycling stability are achieved.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3sznslKgtA%253D%253D&md5=cb719ace996906c08eadedf61644e3dc
  18. 18
    Lu, Y.; Tu, Z.; Archer, L. A. Stable lithium electrodeposition in liquid and nanoporous solid electrolytes. Nat. Mater. 2014, 13, 961,  DOI: 10.1038/nmat4041
    Google Scholar
    18
    Stable lithium electrodeposition in liquid and nanoporous solid electrolytes
    Lu, Yingying; Tu, Zhengyuan; Archer, Lynden A.
    Nature Materials (2014), 13 (10), 961-969CODEN: NMAACR; ISSN:1476-1122. (Nature Publishing Group)
    Rechargeable lithium, sodium and aluminum metal-based batteries are among the most versatile platforms for high-energy, cost-effective electrochem. energy storage. Non-uniform metal deposition and dendrite formation on the neg. electrode during repeated cycles of charge and discharge are major hurdles to commercialization of energy-storage devices based on each of these chemistries. A long-held view is that unstable electrodeposition is a consequence of inherent characteristics of these metals and their inability to form uniform electrodeposits on surfaces with inevitable defects. We report on electrodeposition of lithium in simple liq. electrolytes and in nanoporous solids infused with liq. electrolytes. We find that simple liq. electrolytes reinforced with halogenated salt blends exhibit stable long-term cycling at room temp., often with no signs of deposition instabilities over hundreds of cycles of charge and discharge and thousands of operating hours. We rationalize these observations with the help of surface energy data for the electrolyte/lithium interface and impedance anal. of the interface during different stages of cell operation. Our findings provide support for an important recent theor. prediction that the surface mobility of lithium is significantly enhanced in the presence of lithium halide salts. Our results also show that a high electrolyte modulus is unnecessary for stable electrodeposition of lithium.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhtlahu77J&md5=72567640c740e4e4b584b1e838d4d9d8
  19. 19
    Zhang, X.; Cheng, X.; Chen, X.; Yan, C.; Zhang, Q. Fluoroethylene Carbonate Additives to Render Uniform Li Deposits in Lithium Metal Batteries. Adv. Funct. Mater. 2017, 27, 1605989,  DOI: 10.1002/adfm.201605989
    Google Scholar
    There is no corresponding record for this reference.
  20. 20
    Wang, D.; Zhang, W.; Zheng, W.; Cui, X.; Rojo, T.; Zhang, Q. Towards High-Safe Lithium Metal Anodes: Suppressing Lithium Dendrites via Tuning Surface Energy. Adv. Sci. 2017, 4, 1600168,  DOI: 10.1002/advs.201600168
    Google Scholar
    20
    Towards High-Safe Lithium Metal Anodes: Suppressing Lithium Dendrites via Tuning Surface Energy
    Wang Dong; Zheng Weitao; Cui Xiaoqiang; Zhang Wei; Rojo Teofilo; Zhang Qiang
    Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2017), 4 (1), 1600168 ISSN:2198-3844.
    The formation of lithium dendrites induces the notorious safety issue and poor cycling life of energy storage devices, such as lithium-sulfur and lithium-air batteries. We propose a surface energy model to describe the complex interface between the lithium anode and electrolyte. A universal strategy of hindering formation of lithium dendrites via tuning surface energy of the relevant thin film growth is suggested. The merit of the novel motif lies not only fundamentally a perfect correlation between electrochemistry and thin film fields, but also significantly promotes larger-scale application of lithium-sulfur and lithium-air batteries, as well as other metal batteries (e.g., Zn, Na, K, Cu, Ag, and Sn).
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1c7nsFektg%253D%253D&md5=97708cfbe6ea7cd34edab1a667521da1
  21. 21
    Zhang, Y.; Qian, J.; Xu, W.; Russell, S. M.; Chen, X.; Nasybulin, E.; Bhattacharya, P.; Engelhard, M. H.; Mei, D.; Cao, R.; Ding, F.; Cresce, A. V.; Xu, K.; Zhang, J.-G. Dendrite-Free Lithium Deposition with Self-Aligned Nanorod Structure. Nano Lett. 2014, 14, 6889– 6896,  DOI: 10.1021/nl5039117
    Google Scholar
    21
    Dendrite-Free Lithium Deposition with Self-Aligned Nanorod Structure
    Zhang, Yaohui; Qian, Jiangfeng; Xu, Wu; Russell, Selena M.; Chen, Xilin; Nasybulin, Eduard; Bhattacharya, Priyanka; Engelhard, Mark H.; Mei, Donghai; Cao, Ruiguo; Ding, Fei; Cresce, Arthur V.; Xu, Kang; Zhang, Ji-Guang
    Nano Letters (2014), 14 (12), 6889-6896CODEN: NALEFD; ISSN:1530-6984. (American Chemical Society)
    Suppressing Li dendrite growth is one of the most crit. challenges for the development of Li metal batteries. Here, the authors report for the 1st time the growth of dendrite-free Li films with a self-aligned and highly compacted nanorod structure when the film was deposited in the electrolyte consisting of 1.0M LiPF6 in propylene carbonate with 0.05M CsPF6 as an additive. Evolution of both the surface and the cross-sectional morphologies of the Li films during repeated Li deposition/stripping processes were studied. The formation of the compact Li nanorod structure is preceded by a solid electrolyte interphase (SEI) layer formed on the surface of the substrate. Electrochem. anal. indicates that an initial redn. process occurred at ∼2.05 V vs. Li/Li+ before Li deposition is responsible for the formation of the initial SEI, while the XPS indicates that the presence of CsPF6 additive can largely enhance the formation of LiF in this initial SEI. Hence, the smooth Li deposition in Cs+-contg. electrolyte is the result of a synergistic effect of Cs+ additive and preformed SEI layer. A fundamental understanding on the compn., internal structure, and evolution of Li metal films may lead to new approaches to stabilize the long-term cycling stability of Li metal and other metal anodes for energy storage applications.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhvFOjs7zN&md5=568690f8c450f7a6ff90166875481674
  22. 22
    Mayers, M. Z.; Kaminski, J. W.; Miller, T. F. Suppression of Dendrite Formation via Pulse Charging in Rechargeable Lithium Metal Batteries. J. Phys. Chem. C 2012, 116, 26214– 26221,  DOI: 10.1021/jp309321w
    Google Scholar
    22
    Suppression of Dendrite Formation via Pulse Charging in Rechargeable Lithium Metal Batteries
    Mayers, Matthew Z.; Kaminski, Jakub W.; Miller, Thomas F.
    Journal of Physical Chemistry C (2012), 116 (50), 26214-26221CODEN: JPCCCK; ISSN:1932-7447. (American Chemical Society)
    We introduce a coarse-grained simulation model for the reductive deposition of lithium cations in secondary lithium batteries. The model accounts for the heterogeneous and nonequil. nature of the electrodeposition dynamics, and it enables simulation of the long timescales and lengthscales assocd. with metal dendrite formation. We investigate the effects of applied overpotential and material properties on early-stage dendrite formation, as well as the mol. mechanisms that govern this process. The model confirms that dendrite formation propensity increases with the applied electrode overpotential, and it demonstrates that application of the electrode overpotential in time-dependent pulses leads to dramatic suppression of dendrite formation while reducing the accumulated electrode on-time by as much as 96%. Moreover, the model predicts that time dependence of the applied electrode overpotential can lead to pos., neg., or zero correlation between cation diffusivity in the solid-electrolyte interphase (SEI) and dendrite formation propensity. Anal. of the simulation trajectories reveals that dendrite formation emerges from a competition between the timescales for cation diffusion and redn. at the anode/SEI interface, with lower applied overpotentials and shorter electrode pulse durations shifting this competition in favor of lower dendrite formation propensity. This work provides a mol. basis for understanding and designing pulsing waveforms that mitigate dendrite formation while minimally affecting battery charging times.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xhs12mtrzE&md5=e17f74971b90114acb68bb36063c52ce
  23. 23
    Aryanfar, A.; Brooks, D.; Merinov, B. V.; Goddard, W. A.; Colussi, A. J.; Hoffmann, M. R. Dynamics of Lithium Dendrite Growth and Inhibition: Pulse Charging Experiments and Monte Carlo Calculations. J. Phys. Chem. Lett. 2014, 5, 1721– 1726,  DOI: 10.1021/jz500207a
    Google Scholar
    23
    Dynamics of Lithium Dendrite Growth and Inhibition: Pulse Charging Experiments and Monte Carlo Calculations
    Aryanfar, Asghar; Brooks, Daniel; Merinov, Boris V.; Goddard, William A.; Colussi, Agustin J.; Hoffmann, Michael R.
    Journal of Physical Chemistry Letters (2014), 5 (10), 1721-1726CODEN: JPCLCD; ISSN:1948-7185. (American Chemical Society)
    Short-circuiting via dendrites compromises the reliability of Li-metal batteries. Dendrites ensue from instabilities inherent to electrodeposition that should be amenable to dynamic control. Here, the authors report that by charging a scaled coin-cell prototype with 1 ms pulses followed by 3 ms rest periods the av. dendrite length is shortened ∼2.5 times relative to those grown under continuous charging. Monte Carlo simulations dealing with Li+ diffusion and electromigration reveal that expts. involving 20 ms pulses were ineffective because Li+ migration in the strong elec. fields converging to dendrite tips generates extended depleted layers that cannot be replenished by diffusion during rest periods. Because the application of pulses much shorter than the characteristic time τc approx. O(∼1 ms) for polarizing elec. double layers in the system would approach d.c. charging, probably dendrite propagation can should be inhibited (albeit not suppressed) by pulse charging within appropriate frequency ranges.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXntFOmurw%253D&md5=a49ca2f7ec9d6e627017e04994a96429
  24. 24
    Liu, Q.; Xu, J.; Yuan, S.; Chang, Z.; Xu, D.; Yin, Y.; Li, L.; Zhong, H.; Jiang, Y.; Yan, J.; Zhang, X. Artificial Protection Film on Lithium Metal Anode toward Long-Cycle-Life Lithium-Oxygen Batteries. Adv. Mater. 2015, 27, 5241– 5247,  DOI: 10.1002/adma.201501490
    Google Scholar
    24
    Artificial Protection Film on Lithium Metal Anode toward Long-Cycle-Life Lithium-Oxygen Batteries
    Liu, Qing-Chao; Xu, Ji-Jing; Yuan, Shuang; Chang, Zhi-Wen; Xu, Dan; Yin, Yan-Bin; Li, Lin; Zhong, Hai-Xia; Jiang, Yin-Shan; Yan, Jun-Min; Zhang, Xin-Bo
    Advanced Materials (Weinheim, Germany) (2015), 27 (35), 5241-5247CODEN: ADVMEW; ISSN:0935-9648. (Wiley-VCH Verlag GmbH & Co. KGaA)
    A facile and effective strategy was developed to protect the lithium anode of a secondary lithium battery through fabrication of a protection film on the metal Li anode, in which a fluoroethylene carbonate (I) additive plays a key role in the crucial film-forming additive. As a proof-of-concept expt., even when using conventional Super P (carbon black) cathode, the obtained I-treated Li metal anode endowed Li-O2 batteries with superior cycle stability of >100 stable cycles with a fixed capacity of 1000 mA-h/g at a c.d. of 300 mA/g was obtained, which is more than three times that of the cells with a pristine Li metal anode and Li metal anode treated without I. The significantly improved cycling stability could be attributed to the protective film derived from I decompn.,.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtlWgsL%252FN&md5=e7d030229d8bda40d111e25dbce59847
  25. 25
    Yan, K.; Lee, H.-W.; Gao, T.; Zheng, G.; Yao, H.; Wang, H.; Lu, Z.; Zhou, Y.; Liang, Z.; Liu, Z.; Chu, S.; Cui, Y. Ultrathin Two-Dimensional Atomic Crystals as Stable Interfacial Layer for Improvement of Lithium Metal Anode. Nano Lett. 2014, 14, 6016– 6022,  DOI: 10.1021/nl503125u
    Google Scholar
    25
    Ultrathin Two-Dimensional Atomic Crystals as Stable Interfacial Layer for Improvement of Lithium Metal Anode
    Yan, Kai; Lee, Hyun-Wook; Gao, Teng; Zheng, Guangyuan; Yao, Hongbin; Wang, Haotian; Lu, Zhenda; Zhou, Yu; Liang, Zheng; Liu, Zhongfan; Chu, Steven; Cui, Yi
    Nano Letters (2014), 14 (10), 6016-6022CODEN: NALEFD; ISSN:1530-6984. (American Chemical Society)
    Stable cycling of lithium metal anode is challenging due to the dendritic lithium formation and high chem. reactivity of lithium with electrolyte and nearly all the materials. Here, we demonstrate a promising novel electrode design by growing two-dimensional (2D) at. crystal layers including hexagonal boron nitride (h-BN) and graphene directly on Cu metal current collectors. Lithium ions were able to penetrate through the point and line defects of the 2D layers during the electrochem. deposition, leading to sandwiched lithium metal between ultrathin 2D layers and Cu. The 2D layers afford an excellent interfacial protection of Li metal due to their remarkable chem. stability as well as mech. strength and flexibility, resulting from the strong intralayer bonds and ultrathin thickness. Smooth Li metal deposition without dendritic and mossy Li formation was realized. We showed stable cycling over 50 cycles with Coulombic efficiency ∼97% in org. carbonate electrolyte with c.d. and areal capacity up to the practical value of 2.0 mA/cm2and 5.0 mAh/cm2, resp., which is a significant improvement over the unprotected electrodes in the same electrolyte.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsVekt7jJ&md5=b7cf8adb44da1aedf75c2a104d259fb2
  26. 26
    Liu, Y.; Lin, D.; Yuen, P. Y.; Liu, K.; Xie, J.; Dauskardt, R. H.; Cui, Y. An Artificial Solid Electrolyte Interphase with High Li-Ion Conductivity, Mechanical Strength, and Flexibility for Stable Lithium Metal Anodes. Adv. Mater. 2017, 29, 1605531,  DOI: 10.1002/adma.201605531
    Google Scholar
    There is no corresponding record for this reference.
  27. 27
    Khurana, R.; Schaefer, J. L.; Archer, L. A.; Coates, G. W. Suppression of Lithium Dendrite Growth Using Cross-Linked Polyethylene/Poly(ethylene oxide) Electrolytes: A New Approach for Practical Lithium-Metal Polymer Batteries. J. Am. Chem. Soc. 2014, 136, 7395– 7402,  DOI: 10.1021/ja502133j
    Google Scholar
    27
    Suppression of Lithium Dendrite Growth Using Cross-Linked Polyethylene/Poly(ethylene oxide) Electrolytes: A New Approach for Practical Lithium-Metal Polymer Batteries
    Khurana, Rachna; Schaefer, Jennifer L.; Archer, Lynden A.; Coates, Geoffrey W.
    Journal of the American Chemical Society (2014), 136 (20), 7395-7402CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
    Solid polymer electrolyte (SPE) membranes are a crit. component of high specific energy rechargeable Li-metal polymer (LMP) batteries. SPEs exhibit low volatility and thus increase the safety of Li-based batteries compared to current state-of-the-art Li-ion batteries that use flammable small-mol. electrolytes. However, most SPEs exhibit low ionic cond. at room temp., and often allow the growth of lithium dendrites that short-circuit the batteries. Both of these deficiencies are significant barriers to the commercialization of LMP batteries. Herein a cross-linked polyethylene/poly(ethylene oxide) SPE is reported with both high ionic cond. (> 1.0 × 10-4 S/cm at 25°) and excellent resistance to dendrite growth. It has been proposed that SPEs with shear moduli of the same order of magnitude as lithium could be used to suppress dendrite growth, leading to increased lifetime and safety for LMP batteries. In contrast to the theor. predictions, the low-modulus (G' ≈ 1.0 × 105 Pa at 90°) cross-linked SPEs reported herein exhibit remarkable dendrite growth resistance. These results suggest that a high-modulus SPE is not a requirement for the control of dendrite proliferation.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXmsFWnsrc%253D&md5=71960add56cf11e7e9d858a50b0c6d1c
  28. 28
    Stone, G. M.; Mullin, S. A.; Teran, A. A.; Hallinan, D. T.; Minor, A. M.; Hexemer, A.; Balsara, N. P. Resolution of the Modulus versus Adhesion Dilemma in Solid Polymer Electrolytes for Rechargeable Lithium Metal Batteries. J. Electrochem. Soc. 2012, 159, A222– A227,  DOI: 10.1149/2.030203jes
    Google Scholar
    28
    Resolution of the Modulus versus Adhesion Dilemma in Solid Polymer Electrolytes for Rechargeable Lithium Metal Batteries
    Stone, G. M.; Mullin, S. A.; Teran, A. A.; Hallinan, D. T., Jr.; Minor, A. M.; Hexemer, A.; Balsara, N. P.
    Journal of the Electrochemical Society (2012), 159 (3), A222-A227CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
    The authors present a solid electrolyte that adheres to the Li surface and resists dendrite growth, both of which are needed for the development of high specific energy rechargeable batteries with Li anodes. Nanostructured lamellar block copolymer electrolytes exhibit solid-like properties in the bulk, due to the presence of a randomly oriented granular structure, and liq.-like surface properties due to the formation of perpendicularly oriented lamellae at the Li-electrolyte interface. The amt. of charge that can be passed before short-circuit in a sym. Li-polymer-Li cell with nanostructured polystyrene-block-poly(ethylene oxide) electrolytes is larger than that obtained with homopolymer poly(ethylene oxide) electrolytes by a factor of 11 to 48. Grazing incident small angle x-ray scattering confirms that the microstructure of the block copolymer near the Li-polymer interface has a perpendicular orientation. This orientation leads to a liq.-like behavior of the polymer at the interface due to the liq. cryst. symmetry of block copolymers. This combination of bulk and surface properties enhances the resistance to dendrites while maintaining electrode-electrolyte contact.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XnvVSnsg%253D%253D&md5=45d31f648114ce418df69b70a8b1ab72
  29. 29
    Yue, L.; Ma, J.; Zhang, J.; Zhao, J.; Dong, S.; Liu, Z.; Cui, G.; Chen, L. All solid-state polymer electrolytes for high-performance lithium ion batteries. Energy Storage Mater. 2016, 5, 139– 164,  DOI: 10.1016/j.ensm.2016.07.003
    Google Scholar
    There is no corresponding record for this reference.
  30. 30
    Janek, J.; Zeier, W. G. A solid future for battery development. Nat. Energy 2016, 1, 16141,  DOI: 10.1038/nenergy.2016.141
    Google Scholar
    There is no corresponding record for this reference.
  31. 31
    Li, J.; Ma, C.; Chi, M.; Liang, C.; Dudney, N. J. Solid Electrolyte: the Key for High-Voltage Lithium Batteries. Adv. Energy Mater. 2015, 5, 1401408,  DOI: 10.1002/aenm.201401408
    Google Scholar
    There is no corresponding record for this reference.
  32. 32
    Suzuki, Y.; Kami, K.; Watanabe, K.; Watanabe, A.; Saito, N.; Ohnishi, T.; Takada, K.; Sudo, R.; Imanishi, N. Transparent cubic garnet-type solid electrolyte of Al2O3-doped Li7La3Zr2O12. Solid State Ionics 2015, 278, 172– 176,  DOI: 10.1016/j.ssi.2015.06.009
    Google Scholar
    32
    Transparent cubic garnet-type solid electrolyte of Al2O3-doped Li7La3Zr2O12
    Suzuki, Yosuke; Kami, K.; Watanabe, K.; Watanabe, A.; Saito, N.; Ohnishi, T.; Takada, K.; Sudo, R.; Imanishi, N.
    Solid State Ionics (2015), 278 (), 172-176CODEN: SSIOD3; ISSN:0167-2738. (Elsevier B.V.)
    A transparent garnet-type lithium-ion conducting solid electrolyte of 1.0 wt. % Al2O3-doped Li7La3Zr2O12 (A-LLZ) was prepd. using hot isostatic pressing (HIP). The A-LLZ pellet sintered at 1180°C for 36 h was followed by HIP treatment at 127 MPa and 1180°C under an Ar atm. The bulk cond. of the HIP treated A-LLZ was 9.9 × 10-4 S cm-1 at 25°C. The Li/HIP treated A-LLZ/Li cell showed no short-circuit due to lithium dendrite formation at 0.5 mA cm- 2.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVKmsbrP&md5=7fd10b6e096b6cc875cc5495c59477c7
  33. 33
    Manthiram, A.; Yu, X.; Wang, S. Lithium battery chemistries enabled by solid-state electrolytes. Nat. Rev. Mater. 2017, 2, 16103,  DOI: 10.1038/natrevmats.2016.103
    Google Scholar
    33
    Lithium battery chemistries enabled by solid-state electrolytes
    Manthiram, Arumugam; Yu, Xingwen; Wang, Shaofei
    Nature Reviews Materials (2017), 2 (3), 16103CODEN: NRMADL; ISSN:2058-8437. (Nature Publishing Group)
    Solid-state electrolytes are attracting increasing interest for electrochem. energy storage technologies. In this Review, we provide a background overview and discuss the state of the art, ion-transport mechanisms and fundamental properties of solid-state electrolyte materials of interest for energy storage applications. We focus on recent advances in various classes of battery chemistries and systems that are enabled by solid electrolytes, including all-solid-state lithium-ion batteries and emerging solid-electrolyte lithium batteries that feature cathodes with liq. or gaseous active materials (for example, lithium-air, lithium-sulfur and lithium-bromine systems). A low-cost, safe, aq. electrochem. energy storage concept with a 'mediator-ion' solid electrolyte is also discussed. Advanced battery systems based on solid electrolytes would revitalize the rechargeable battery field because of their safety, excellent stability, long cycle lives and low cost. However, great effort will be needed to implement solid-electrolyte batteries as viable energy storage systems. In this context, we discuss the main issues that must be addressed, such as achieving acceptable ionic cond., electrochem. stability and mech. properties of the solid electrolytes, as well as a compatible electrolyte/electrode interface.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXislGitr0%253D&md5=be4704bc600127083842361f9e75c578
  34. 34
    Kamaya, N.; Homma, K.; Yamakawa, Y.; Hirayama, M.; Kanno, R.; Yonemura, M.; Kamiyama, T.; Kato, Y.; Hama, S.; Kawamoto, K.; Mitsui, A. A lithium superionic conductor. Nat. Mater. 2011, 10, 682– 686,  DOI: 10.1038/nmat3066
    Google Scholar
    34
    A lithium superionic conductor
    Kamaya, Noriaki; Homma, Kenji; Yamakawa, Yuichiro; Hirayama, Masaaki; Kanno, Ryoji; Yonemura, Masao; Kamiyama, Takashi; Kato, Yuki; Hama, Shigenori; Kawamoto, Koji; Mitsui, Akio
    Nature Materials (2011), 10 (9), 682-686CODEN: NMAACR; ISSN:1476-1122. (Nature Publishing Group)
    Batteries are a key technol. in modern society. They are used to power elec. and hybrid elec. vehicles and to store wind and solar energy in smart grids. Electrochem. devices with high energy and power densities can currently be powered only by batteries with org. liq. electrolytes. However, such batteries require relatively stringent safety precautions, making large-scale systems complicated and expensive. The application of solid electrolytes is currently limited because they attain practically useful conductivities (10-2 S/cm) only at 50-80°, which is one order of magnitude lower than those of org. liq. electrolytes. Here, the authors report a Li superionic conductor, Li10GeP2S12 that has a new 3-dimensional framework structure. It exhibits an extremely high Li ionic cond. of 12 mS/cm at room temp. This represents the highest cond. achieved in a solid electrolyte, exceeding even those of liq. org. electrolytes. This new solid-state battery electrolyte has many advantages in terms of device fabrication (facile shaping, patterning and integration), stability (non-volatile), safety (non-explosive) and excellent electrochem. properties (high cond. and wide potential window).
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXpsFaisLc%253D&md5=30637e2968f742a3e1da5f7fa4250644
  35. 35
    Kato, Y.; Hori, S.; Saito, T.; Suzuki, K.; Hirayama, M.; Mitsui, A.; Yonemura, M.; Iba, H.; Kanno, R. High-power all-solid-state batteries using sulfide superionic conductors. Nat. Energy 2016, 1, 16030,  DOI: 10.1038/nenergy.2016.30
    Google Scholar
    35
    High-power all-solid-state batteries using sulfide superionic conductors
    Kato, Yuki; Hori, Satoshi; Saito, Toshiya; Suzuki, Kota; Hirayama, Masaaki; Mitsui, Akio; Yonemura, Masao; Iba, Hideki; Kanno, Ryoji
    Nature Energy (2016), 1 (4), 16030CODEN: NEANFD; ISSN:2058-7546. (Nature Publishing Group)
    Compared with Li-ion batteries with liq. electrolytes, all-solid-state batteries offer an attractive option owing to their potential in improving the safety and achieving both high power and high energy densities. Despite extensive research efforts, the development of all-solid-state batteries still falls short of expectation largely because of the lack of suitable candidate materials for the electrolyte required for practical applications. Here the authors report Li superionic conductors with an exceptionally high cond. (25 mS cm-1 for Li9.54Si1.74P1.44S11.7Cl0.3), as well as high stability ( ∼0 V vs. Li metal for Li9.6P3S12). A fabricated all-solid-state cell based on this Li conductor has very small internal resistance, esp. at 100 oC. The cell possesses high specific power that is superior to that of conventional cells with liq. electrolytes. Stable cycling with a high c.d. of 18 C (charging/discharging in just 3 min; where C is the C-rate) is also demonstrated.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVekurs%253D&md5=cc1210221e70bc3e6c06e015effc70de
  36. 36
    Kerman, K.; Luntz, A.; Viswanathan, V.; Chiang, Y.-M.; Chen, Z. Review-Practical Challenges Hindering the Development of Solid State Li Ion Batteries. J. Electrochem. Soc. 2017, 164, A1731– A1744,  DOI: 10.1149/2.1571707jes
    Google Scholar
    36
    Review-Practical Challenges Hindering the Development of Solid State Li Ion Batteries
    Kerman, Kian; Luntz, Alan; Viswanathan, Venkatasubramanian; Chiang, Yet-Ming; Chen, Zhebo
    Journal of the Electrochemical Society (2017), 164 (7), A1731-A1744CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
    Solid state electrolyte systems boasting Li+ cond. of >10 mS cm-1 at room temp. have opened the potential for developing a solid state battery with power and energy densities that are competitive with conventional liq. electrolyte systems. The primary focus of this review is twofold. First, differences in Li penetration resistance in solid state systems are discussed, and kinetic limitations of the solid state interface are highlighted. Second, technol. challenges assocd. with processing such systems in relevant form factors are elucidated, and architectures needed for cell level devices in the context of product development are reviewed. Specific research vectors that provide high value to advancing solid state batteries are outlined and discussed.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVCitLvI&md5=99cf3345659e25c91eb25ed7090d1403
  37. 37
    Sharafi, A.; Yu, S.; Naguib, M.; Lee, M.; Ma, C.; Meyer, H. M.; Nanda, J.; Chi, M.; Siegel, D. J.; Sakamoto, J. Impact of air exposure and surface chemistry on Li-Li7La3Zr2O12 interfacial resistance. J. Mater. Chem. A 2017, 5, 13475– 13487,  DOI: 10.1039/C7TA03162A
    Google Scholar
    37
    Impact of air exposure and surface chemistry on Li-Li7La3Zr2O12 interfacial resistance
    Sharafi, Asma; Yu, Seungho; Naguib, Michael; Lee, Marcus; Ma, Cheng; Meyer, Harry M.; Nanda, Jagjit; Chi, Maiofang; Siegel, Donald J.; Sakamoto, Jeff
    Journal of Materials Chemistry A: Materials for Energy and Sustainability (2017), 5 (26), 13475-13487CODEN: JMCAET; ISSN:2050-7496. (Royal Society of Chemistry)
    Li7La3Zr2O12 (LLZO) is a promising solid-state electrolyte that could enable solid-state-batteries (SSB) using metallic Li anodes. For a SSB to be viable, the stability and charge transfer kinetics at the Li-LLZO interface should foster facile plating and stripping of Li. Contrary to these goals, recent studies have reported high Li-LLZO interfacial resistance which was attributed to a contamination layer that forms upon exposure of LLZO to air. This study clarifies the mechanisms and consequences assocd. with air exposure of LLZO; addnl., strategies to minimize these effects are described. First-principles calcns. reveal that LLZO readily reacts with humid air; the most favorable reaction pathway involves protonation of LLZO and formation of Li2CO3. XPS, SEM, Raman spectroscopy, and transmission electron microscopy were used to characterize the surface and subsurface chem. of LLZO as a function of relative humidity and exposure time. Electrochem. impedance spectroscopy was used to measure the Li-LLZO interfacial resistance as a function of surface contamination. These data indicate that air exposure-induced contamination impacts the interfacial resistance significantly, when exposure time exceeds 24 h. The results of this study provide valuable insight into the sensitivity of LLZO to air and how the effects of air contamination can be reversed.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVWmtrnE&md5=ba7e873b21db6b50f59e450c3d6f6ec2
  38. 38
    Sharafi, A.; Haslam, C. G.; Kerns, R. D.; Wolfenstine, J.; Sakamoto, J. Controlling and correlating the effect of grain size with the mechanical and electrochemical properties of Li7La3Zr2O12 solid-state electrolyte. J. Mater. Chem. A 2017, 5, 21491– 21504,  DOI: 10.1039/C7TA06790A
    Google Scholar
    38
    Controlling and correlating the effect of grain size with the mechanical and electrochemical properties of Li7La3Zr2O12 solid-state electrolyte
    Sharafi, Asma; Haslam, Catherine G.; Kerns, Robert D.; Wolfenstine, Jeff; Sakamoto, Jeff
    Journal of Materials Chemistry A: Materials for Energy and Sustainability (2017), 5 (40), 21491-21504CODEN: JMCAET; ISSN:2050-7496. (Royal Society of Chemistry)
    Li7La3Zr2O12 (LLZO) solid-state electrolyte is garnering interest due to its potential to enable solid-state batteries (SSBs) using metallic Li anodes. However, Li metal propagates along LLZO grain boundaries at high Li plating current densities (above the crit. c.d., CCD). In the present study, we examd. whether microstructural aspects, such as grain size, could influence mech. and electrochem. properties thereby affecting the CCD. A unique densification technique (heating between 1100 and 1300 °C) was used to control grain size. Electron backscatter diffraction detd. that the grain size and the misorientation angle varied from 5 to 600 μm and 20 to 40°, resp. Vickers indentation was used to characterize the mech. properties and revealed that hardness decreased (9.9-6.8 GPa) with increasing grain size, but the fracture toughness was invariant (0.6 MPa m-1/2) at grain sizes ≥40 μm. DC and AC techniques were used to measure and correlate the CCD with grain size and showed that the CCD increased with increasing grain size achieving a max. of 0.6 mA cm-2. We believe the implications of this work could be far-reaching in that they represent a significant step towards understanding the mechanism(s) that control the stability of the Li-LLZO interface and a rational approach to increase the CCD in SSBs.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhs1Sksb%252FP&md5=eccb1f1d8cfb3d13b2237d27793aa772
  39. 39
    Monroe, C.; Newman, J. The Impact of Elastic Deformation on Deposition Kinetics at Lithium/Polymer Interfaces. J. Electrochem. Soc. 2005, 152, A396– A404,  DOI: 10.1149/1.1850854
    Google Scholar
    39
    The impact of elastic deformation on deposition kinetics at lithium/polymer interfaces
    Monroe, Charles; Newman, John
    Journal of the Electrochemical Society (2005), 152 (2), A396-A404CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
    Past theories of electrode stability assume that the surface tension resists the amplification of surface roughness at cathodes and show that instability at lithium/liq. interfaces cannot be prevented by surface forces alone. This work treats interfacial stability in lithium/polymer systems where the electrolyte is solid. Linear elasticity theory is employed to compute the addnl. effect of bulk mech. forces on electrode stability. The lithium and polymer are treated as Hookean elastic materials, characterized by their shear moduli and Poisson's ratios. Two-dimensional displacement distributions that satisfy force balances across a periodically deforming interface are derived; these allow computation of the stress and surface-tension forces. The incorporation of elastic effects into a kinetic model demonstrates regimes of electrolyte mech. properties where amplification of surface roughness can be inhibited. For a polymer material with Poisson's ratio similar to poly(ethylene oxide), interfacial roughening is mech. suppressed when the separator shear modulus is about twice that of lithium.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXhs1KktLc%253D&md5=e878820c6a811396757bd7435bdb40ad
  40. 40
    Ahmad, Z.; Viswanathan, V. Stability of Electrodeposition at Solid-Solid Interfaces and Implications for Metal Anodes. Phys. Rev. Lett. 2017, 119, 056003,  DOI: 10.1103/PhysRevLett.119.056003
    Google Scholar
    40
    Stability of electrodeposition at solid-solid interfaces and implications for metal anodes
    Ahmad, Zeeshan; Viswanathan, Venkatasubramanian
    Physical Review Letters (2017), 119 (5), 056003/1-056003/6CODEN: PRLTAO; ISSN:1079-7114. (American Physical Society)
    We generalize the conditions for stable electrodeposition at isotropic solid-solid interfaces using a kinetic model which incorporates the effects of stresses and surface tension at the interface. We develop a stability diagram that shows two regimes of stability: a previously known pressure-driven mechanism and a new d.-driven stability mechanism that is governed by the relative d. of metal in the two phases. We show that inorg. solids and solid polymers generally do not lead to stable electrodeposition, and provide design guidelines for achieving stable electrodeposition.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhs1SrtbrP&md5=b71331ba73f970ec5c45f52cdc08ecad
  41. 41
    Diggle, J. W.; Despic, A. R.; Bockris, J. O. The Mechanism of the Dendritic Electrocrystallization of Zinc. J. Electrochem. Soc. 1969, 116, 1503– 1514,  DOI: 10.1149/1.2411588
    Google Scholar
    41
    Mechanism of the dendritic electrocrystallization of zinc
    Diggle, J. W.; Despic, A. R.; Bockris, J. O'M.
    Journal of the Electrochemical Society (1969), 116 (11), 1503-14CODEN: JESOAN; ISSN:0013-4651.
    Measurements were made of the growth rate of Zn dendrites in alk. zincate solns. as a function of overpotential (η), concn. (c), and temp. (T). The tip radii were measured by electron microscopy. At const. potential, an initiation time of between 5 and 100 min is observed, depending on η, c, and T. The total current to base and dendrite was independent of time until a time *aui, where τi < τd (the time for initiation obtained from the growth rate vs. time relation). Thereafter, i is proportional to t2. A crit. overpotential was detd., -75 mv. >ηcrit> -85 mv. Below this ηcrit, sponge was formed. Dendrites were observed up to η = -160 mv.; above this the deposition was heavy sponge. At a given c, the growth rate of a given dendrite increased with η according to an exponential law. The growing tip is parabolic, where 10-5 < γtip < 10-4 cm. No twinning was observed. The basic model used depended on the increase in c.d. possible for an electrodic reaction when the diffusion current depends on a radius of curvature of the substrate, rather than the linear diffusion layer thickness, δ. When the tip of a dendrite-precursor attains this condition, its growth is released from the diffusion control characteristic of it in the predendrite situation, and it grows further under predominantly activation control at a rate far greater than that possible in any other direction, where the radii of curvature are much greater. The Gibbs radius-dependent overpotential term is also present, although it has a minimized influence. The initiation of the dendrite is treated in terms of growing pyramids on the substrate surface. At 1st the growth is linear-diffusion controlled, but the rotation of the spiral, within the linear diffusion boundary surrounding the sphere, gives rise to a decrease of the effective radius (γ) of curvature of the dendrite tip until the value γ<0.1 δ is attained, which is effectively the condition for the dendrite initiation. The theory of the propagation in terms of the activation, diffusion, and Gibbs overpotential is consistent, in terms of τd, with expt. A derived growth-time line is also numerically consistent with expt. The dendrite growth rate as a function of c and η are numerically calcd. with reasonable consistency. The tip radius can also be approx. calcd. in terms of the present model.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaE3cXos1yl&md5=48077f26ea20fa76b2cc78e39c4f1309
  42. 42
    Monroe, C.; Newman, J. Dendrite Growth in Lithium/Polymer Systems: A Propagation Model for Liquid Electrolytes under Galvanostatic Conditions. J. Electrochem. Soc. 2003, 150, A1377– A1384,  DOI: 10.1149/1.1606686
    Google Scholar
    42
    Dendrite Growth in Lithium/Polymer Systems
    Monroe, Charles; Newman, John
    Journal of the Electrochemical Society (2003), 150 (10), A1377-A1384CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
    Dendrite growth in a parallel-electrode Li/polymer battery during galvanostatic charging was modeled. The growth model is surface-energy controlled, incorporating the effect of dendrite tip curvature into the dendrite growth kinetics. Using data representative of the oxymethylene-linked poly(ethylene oxide)/LiTFSI system, dendrites accelerate across cells under all conditions, and growth is always slowed by lowering the c.d. Cell shorting occurs during typical charges at current densities >75% of the limiting current. Increased interelectrode distance slows failure, but the advantages decrease as distance increases. A factor of 1000 increase in surface forces delays cell failure by only 6%. While larger diffusion coeffs. usually extend the time to cell failure, this trend is not consistent at high transference nos.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3sXnt1OmtLw%253D&md5=178ec3a3e854d05aede7708d68c7957a
  43. 43
    Monroe, C.; Newman, J. The Effect of Interfacial Deformation on Electrodeposition Kinetics. J. Electrochem. Soc. 2004, 151, A880– A886,  DOI: 10.1149/1.1710893
    Google Scholar
    43
    The Effect of Interfacial Deformation on Electrodeposition Kinetics
    Monroe, Charles; Newman, John
    Journal of the Electrochemical Society (2004), 151 (6), A880-A886CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
    Mullins-Sekerka linear stability anal. and the Barton and Bockris dendrite-propagation model are popular methods used to describe cathodic roughening and dendritic growth. These commonly cited theories employ kinetic relations that differ in math. form, but both contain the effects of surface tension and local concn. deviations induced by surface roughening. Here, a kinetic model is developed which addnl. includes mech. forces such as elasticity, viscous drag, and pressure, showing their effect on exchange current densities and potentials at roughening interfaces. The proposed expression describes the c.d. in terms of applied overpotential at deformed interfaces with arbitrary three-dimensional interfacial geometry. Both the Mullins-Sekerka and the Barton-Bockris kinetics can be derived as special cases of the general expression, thereby validating the proposed model and elucidating the fundamental assumptions on which the 2 previous theories rely.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXktVGkt70%253D&md5=22ed18c5cf280522d5d26e2bc30cb6bc
  44. 44
    Curtarolo, S.; Hart, G. L. W.; Nardelli, M. B.; Mingo, N.; Sanvito, S.; Levy, O. The high-throughput highway to computational materials design. Nat. Mater. 2013, 12, 191,  DOI: 10.1038/nmat3568
    Google Scholar
    44
    The high-throughput highway to computational materials design
    Curtarolo, Stefano; Hart, Gus L. W.; Nardelli, Marco Buongiorno; Mingo, Natalio; Sanvito, Stefano; Levy, Ohad
    Nature Materials (2013), 12 (3), 191-201CODEN: NMAACR; ISSN:1476-1122. (Nature Publishing Group)
    A review. High-throughput computational materials design is an emerging area of materials science. By combining advanced thermodn. and electronic-structure methods with intelligent data mining and database construction, and exploiting the power of current supercomputer architectures, scientists generate, manage and analyze enormous data repositories for the discovery of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and opportunities that lie ahead.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXislWju7c%253D&md5=5e116fbafda8e8437ccd0fdf7304d939
  45. 45
    Saal, J. E.; Kirklin, S.; Aykol, M.; Meredig, B.; Wolverton, C. Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD). JOM 2013, 65, 1501– 1509,  DOI: 10.1007/s11837-013-0755-4
    Google Scholar
    45
    Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
    Saal, James E.; Kirklin, Scott; Aykol, Muratahan; Meredig, Bryce; Wolverton, C.
    JOM (2013), 65 (11), 1501-1509CODEN: JOMMER; ISSN:1047-4838. (Springer)
    A review. High-throughput d. functional theory (HT DFT) is fast becoming a powerful tool for accelerating materials design and discovery by the amassing tens and even hundreds of thousands of DFT calcns. in large databases. Complex materials problems can be approached much more efficiently and broadly through the sheer quantity of structures and chemistries available in such databases. Our HT DFT database, the Open Quantum Materials Database (OQMD), contains over 200,000 DFT calcd. crystal structures and will be freely available for public use at http://oqmd.org. In this review, we describe the OQMD and its use in five materials problems, spanning a wide range of applications and materials types: (I) Li-air battery combination catalyst/electrodes, (II) Li-ion battery anodes, (III) Li-ion battery cathode coatings reactive with HF, (IV) Mg-alloy long-period stacking ordered (LPSO) strengthening ppts., and (V) training a machine learning model to predict new stable ternary compds.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsFentbzK&md5=feaac43dc6ff4c2d7a7a4c94f0b58c2b
  46. 46
    Pilania, G.; Wang, C.; Jiang, X.; Rajasekaran, S.; Ramprasad, R. Accelerating materials property predictions using machine learning. Sci. Rep. 2013, 3, 2810,  DOI: 10.1038/srep02810
    Google Scholar
    46
    Accelerating materials property predictions using machine learning
    Pilania Ghanshyam; Wang Chenchen; Jiang Xun; Rajasekaran Sanguthevar; Ramprasad Ramamurthy
    Scientific reports (2013), 3 (), 2810 ISSN:.
    The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces, and can significantly accelerate the discovery of new application-specific materials.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2c%252FltlCqtQ%253D%253D&md5=3475edb4dda3d93baae0d552d482b187
  47. 47
    Liu, Y.; Zhao, T.; Ju, W.; Shi, S. Materials discovery and design using machine learning. J. Materiomics 2017, 3, 159– 177,  DOI: 10.1016/j.jmat.2017.08.002
    Google Scholar
    There is no corresponding record for this reference.
  48. 48
    Gómez-Bombarelli, R.; Wei, J. N.; Duvenaud, D.; Hernández-Lobato, J. M.; Sánchez-Lengeling, B.; Sheberla, D.; Aguilera-Iparraguirre, J.; Hirzel, T. D.; Adams, R. P.; Aspuru-Guzik, A. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Cent. Sci. 2018, 4, 268– 276,  DOI: 10.1021/acscentsci.7b00572
    Google Scholar
    48
    Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
    Gomez-Bombarelli, Rafael; Wei, Jennifer N.; Duvenaud, David; Hernandez-Lobato, Jose Miguel; Sanchez-Lengeling, Benjamin; Sheberla, Dennis; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D.; Adams, Ryan P.; Aspuru-Guzik, Alan
    ACS Central Science (2018), 4 (2), 268-276CODEN: ACSCII; ISSN:2374-7951. (American Chemical Society)
    We report a method to convert discrete representations of mols. to and from a multidimensional continuous representation. This model allows us to generate new mols. for efficient exploration and optimization through open-ended spaces of chem. compds. A deep neural network was trained on hundreds of thousands of existing chem. structures to construct three coupled functions: an encoder, a decoder, and a predictor. The encoder converts the discrete representation of a mol. into a real-valued continuous vector, and the decoder converts these continuous vectors back to discrete mol. representations. The predictor ests. chem. properties from the latent continuous vector representation of the mol. Continuous representations of mols. allow us to automatically generate novel chem. structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chem. structures, or interpolating between mols. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compds. We demonstrate our method in the domain of drug-like mols. and also in a set of mols. with fewer that nine heavy atoms.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXntlWquw%253D%253D&md5=322d9ff569fc9c8831e91d915104d985
  49. 49
    de Jong, M.; Chen, W.; Notestine, R.; Persson, K.; Ceder, G.; Jain, A.; Asta, M.; Gamst, A. A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds. Sci. Rep. 2016, 6, 34256,  DOI: 10.1038/srep34256
    Google Scholar
    49
    A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
    de Jong, Maarten; Chen, Wei; Notestine, Randy; Persson, Kristin; Ceder, Gerbrand; Jain, Anubhav; Asta, Mark; Gamst, Anthony
    Scientific Reports (2016), 6 (), 34256CODEN: SRCEC3; ISSN:2045-2322. (Nature Publishing Group)
    Materials scientists increasingly employ machine or statistical learning (SL) techniques to accelerate materials discovery and design. Such pursuits benefit from pooling training data across, and thus being able to generalize predictions over, k-nary compds. of diverse chemistries and structures. This work presents a SL framework that addresses challenges in materials science applications, where datasets are diverse but of modest size, and extreme values are often of interest. Our advances include the application of power or Holder means to construct descriptors that generalize over chem. and crystal structure, and the incorporation of multivariate local regression within a gradient boosting framework. The approach is demonstrated by developing SL models to predict bulk and shear moduli (K and G, resp.) for polycryst. inorg. compds., using 1,940 compds. from a growing database of calcd. elastic moduli for metals, semiconductors and insulators. The usefulness of the models is illustrated by screening for superhard materials.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xhs1amtL%252FO&md5=dbe3625fe35277822c6ae92c7dcbfee7
  50. 50
    Isayev, O.; Oses, C.; Toher, C.; Gossett, E.; Curtarolo, S.; Tropsha, A. Universal fragment descriptors for predicting properties of inorganic crystals. Nat. Commun. 2017, 8, 15679,  DOI: 10.1038/ncomms15679
    Google Scholar
    50
    Universal fragment descriptors for predicting properties of inorganic crystals
    Isayev, Olexandr; Oses, Corey; Toher, Cormac; Gossett, Eric; Curtarolo, Stefano; Tropsha, Alexander
    Nature Communications (2017), 8 (), 15679CODEN: NCAOBW; ISSN:2041-1723. (Nature Publishing Group)
    A review. Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calcns. is combined with Quant. Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temp. and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorg. cryst. material, reciprocating the available thermomech. exptl. data. The universality of the approach is attributed to the construction of the descriptors: Property-Labeled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXpt1Oksb8%253D&md5=543e2069263b9b1b1a121667866e2c5a
  51. 51
    Fujimura, K.; Seko, A.; Koyama, Y.; Kuwabara, A.; Kishida, I.; Shitara, K.; Fisher, C. A. J.; Moriwake, H.; Tanaka, I. Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms. Adv. Energy Mater. 2013, 3, 980– 985,  DOI: 10.1002/aenm.201300060
    Google Scholar
    51
    Accelerated materials design of lithium superionic conductors based on first-principles calculations and machine learning algorithms
    Fujimura, Koji; Seko, Atsuto; Koyama, Yukinori; Kuwabara, Akihide; Kishida, Ippei; Shitara, Kazuki; Fisher, Craig A. J.; Moriwake, Hiroki; Tanaka, Isao
    Advanced Energy Materials (2013), 3 (8), 980-985CODEN: ADEMBC; ISSN:1614-6840. (Wiley-Blackwell)
    In this article, results of systematic sets of first-principles calcns. based on the cluster expansion method, as well as first-principles mol. dynamics (FPMD) simulations carried out to calc. lithium-ion conductivities at high temp., for a diverse range of compns. is studied. A machine-learning technique is used to combine theor. and exptl. datasets to predict the cond. of each compn. at 373 K. The insights obtained show that an iterative combination of first-principles calcns. and focused expts. can greatly accelerate the materials design process by enabling a wide compositional and structural phase space to be examd. efficiently.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtlWhsbzM&md5=4ec409403b25e39e04cc760523694fa3
  52. 52
    Sendek, A. D.; Yang, Q.; Cubuk, E. D.; Duerloo, K.-A. N.; Cui, Y.; Reed, E. J. Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials. Energy Environ. Sci. 2017, 10, 306– 320,  DOI: 10.1039/C6EE02697D
    Google Scholar
    52
    Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials
    Sendek, Austin D.; Yang, Qian; Cubuk, Ekin D.; Duerloo, Karel-Alexander N.; Cui, Yi; Reed, Evan J.
    Energy & Environmental Science (2017), 10 (1), 306-320CODEN: EESNBY; ISSN:1754-5706. (Royal Society of Chemistry)
    We present a new type of large-scale computational screening approach for identifying promising candidate materials for solid state electrolytes for lithium ion batteries that is capable of screening all known lithium contg. solids. To be useful for batteries, high performance solid state electrolyte materials must satisfy many requirements at once, an optimization that is difficult to perform exptl. or with computationally expensive ab initio techniques. We first screen 12 831 lithium contg. cryst. solids for those with high structural and chem. stability, low electronic cond., and low cost. We then develop a data-driven ionic cond. classification model using logistic regression for identifying which candidate structures are likely to exhibit fast lithium conduction based on exptl. measurements reported in the literature. The screening reduces the list of candidate materials from 12 831 down to 21 structures that show promise as electrolytes, few of which have been examd. exptl. We discover that none of our simple atomistic descriptor functions alone provide predictive power for ionic cond., but a multi-descriptor model can exhibit a useful degree of predictive power. We also find that screening for structural stability, chem. stability and low electronic cond. eliminates 92.2% of all Li-contg. materials and screening for high ionic cond. eliminates a further 93.3% of the remainder. Our screening utilizes structures and electronic information contained in the Materials Project database.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhvFymtbrP&md5=97cc808518346d0a599f623a2b35b7e4
  53. 53
    Evans, J. D.; Coudert, F.-X. Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning. Chem. Mater. 2017, 29, 7833– 7839,  DOI: 10.1021/acs.chemmater.7b02532
    Google Scholar
    53
    Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning
    Evans, Jack D.; Coudert, Francois-Xavier
    Chemistry of Materials (2017), 29 (18), 7833-7839CODEN: CMATEX; ISSN:0897-4756. (American Chemical Society)
    We show here that machine learning is a powerful new tool for predicting the elastic response of zeolites. We built our machine learning approach relying on geometric features only, which are related to local geometry, structure, and porosity of a zeolite, to predict bulk and shear moduli of zeolites with an accuracy exceeding that of force field approaches. The development of this model has illustrated clear correlations between characteristic features of a zeolite and elastic moduli, providing exceptional insight into the mechanics of zeolitic frameworks. Finally, we employ this methodol. to predict the elastic response of 590,448 hypothetical zeolites, and the results of this massive database provide clear evidence of stability trends in porous materials.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtl2lsLfK&md5=5745ca383f3204d7c606d9eec2953d22
  54. 54
    Ahmad, Z.; Viswanathan, V. Role of anisotropy in determining stability of electrodeposition at solid-solid interfaces. Phys. Rev. Materials 2017, 1, 055403,  DOI: 10.1103/PhysRevMaterials.1.055403
    Google Scholar
    There is no corresponding record for this reference.
  55. 55
    Xu, C.; Ahmad, Z.; Aryanfar, A.; Viswanathan, V.; Greer, J. R. Enhanced strength and temperature dependence of mechanical properties of Li at small scales and its implications for Li metal anodes. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 57– 61,  DOI: 10.1073/pnas.1615733114
    Google Scholar
    55
    Enhanced strength and temperature dependence of mechanical properties of Li at small scales and its implications for Li metal anodes
    Xu, Chen; Ahmad, Zeeshan; Aryanfar, Asghar; Viswanathan, Venkatasubramanian; Greer, Julia R.
    Proceedings of the National Academy of Sciences of the United States of America (2017), 114 (1), 57-61CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
    Most next-generation Li ion battery chemistries require a functioning lithium metal (Li) anode. However, its application in secondary batteries has been inhibited because of uncontrollable dendrite growth during cycling. Mech. suppression of dendrite growth through solid polymer electrolytes (SPEs) or through robust separators has shown the most potential for alleviating this problem. Studies of the mech. behavior of Li at any length scale and temp. are limited because of its extreme reactivity, which renders sample prepn., transfer, microstructure characterization, and mech. testing extremely challenging. We conduct nanomech. expts. in an in situ scanning electron microscope and show that micrometer-sized Li attains extremely high strengths of 105 MPa at room temp. and of 35 MPa at 90 °C. We demonstrate that single-cryst. Li exhibits a power-law size effect at the micrometer and submicrometer length scales, with the strengthening exponent of -0.68 at room temp. and of -1.00 at 90 °C. We also report the elastic and shear moduli as a function of crystallog. orientation gleaned from expts. and first-principles calcns., which show a high level of anisotropy up to the m.p., where the elastic and shear moduli vary by a factor of ∼4 between the stiffest and most compliant orientations. The emergence of such high strengths in small-scale Li and sensitivity of this metal's stiffness to crystallog. orientation help explain why the existing methods of dendrite suppression have been mainly unsuccessful and have significant implications for practical design of future-generation batteries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XitFWls7fM&md5=65404fc79b4b340710a0a4c287094cf0
  56. 56
    Shi, F.; Pei, A.; Vailionis, A.; Xie, J.; Liu, B.; Zhao, J.; Gong, Y.; Cui, Y. Strong texturing of lithium metal in batteries. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 12138– 12143,  DOI: 10.1073/pnas.1708224114
    Google Scholar
    56
    Strong texturing of lithium metal in batteries
    Shi, Feifei; Pei, Allen; Vailionis, Arturas; Xie, Jin; Liu, Bofei; Zhao, Jie; Gong, Yongji; Cui, Yi
    Proceedings of the National Academy of Sciences of the United States of America (2017), 114 (46), 12138-12143CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
    Lithium, with its high theor. specific capacity and lowest electrochem. potential, was recognized as the ultimate neg. electrode material for next-generation lithium-based high-energy-d. batteries. However, a key challenge that has yet to be overcome is the inferior reversibility of Li plating and stripping, typically thought to be related to the uncontrollable morphol. evolution of the Li anode during cycling. Here we show that Li-metal texturing (preferential crystallog. orientation) occurs during electrochem. deposition, which governs the morphol. change of the Li anode. X-ray diffraction pole-figure anal. demonstrates that the texture of Li deposits is primarily dependent on the type of additive or cross-over mol. from the cathode side. With adsorbed additives, like LiNO3 and polysulfide, the lithium deposits are strongly textured, with Li (110) planes parallel to the substrate, and thus exhibit uniform, rounded morphol. A growth diagram of lithium deposits is given to connect various texture and morphol. scenarios for different battery electrolytes. This understanding of lithium electrocrystn. from the crystallog. point of view provides significant insight for future lithium anode materials design in high-energy-d. batteries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhslemsrjO&md5=ca7238bf137ef10cd147f88226c28000
  57. 57
    Wang, Y.; Richards, W. D.; Ong, S. P.; Miara, L. J.; Kim, J. C.; Mo, Y.; Ceder, G. Design principles for solid-state lithium superionic conductors. Nat. Mater. 2015, 14, 1026– 1031,  DOI: 10.1038/nmat4369
    Google Scholar
    57
    Design principles for solid-state lithium superionic conductors
    Wang, Yan; Richards, William Davidson; Ong, Shyue Ping; Miara, Lincoln J.; Kim, Jae Chul; Mo, Yifei; Ceder, Gerbrand
    Nature Materials (2015), 14 (10), 1026-1031CODEN: NMAACR; ISSN:1476-1122. (Nature Publishing Group)
    Lithium solid electrolytes can potentially address two key limitations of the org. electrolytes used in today's lithium-ion batteries, namely, their flammability and limited electrochem. stability. However, achieving a Li+ cond. in the solid state comparable to existing liq. electrolytes (>1 mS cm-1) is particularly challenging. In this work, we reveal a fundamental relationship between anion packing and ionic transport in fast Li-conducting materials and expose the desirable structural attributes of good Li-ion conductors. We find that an underlying body-centered cubic-like anion framework, which allows direct Li hops between adjacent tetrahedral sites, is most desirable for achieving high ionic cond., and that indeed this anion arrangement is present in several known fast Li-conducting materials and other fast ion conductors. These findings provide important insight towards the understanding of ionic transport in Li-ion conductors and serve as design principles for future discovery and design of improved electrolytes for Li-ion batteries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtlCksb%252FI&md5=114ad3946493cf35ef3ee5d65e37c2d7
  58. 58
    Shannon, R. D. Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides. Acta Crystallogr., Sect. A: Cryst. Phys., Diffr., Theor. Gen. Crystallogr. 1976, 32, 751– 767,  DOI: 10.1107/S0567739476001551
    Google Scholar
    There is no corresponding record for this reference.
  59. 59
    Gotoh, K.; Finney, J. L. Statistical geometrical approach to random packing density of equal spheres. Nature 1974, 252, 202,  DOI: 10.1038/252202a0
    Google Scholar
    There is no corresponding record for this reference.
  60. 60
    Stepanyuk, V.; Szasz, A.; Katsnelson, A.; Trushin, O.; Müller, H.; Kirchmayr, H. Microstructure and its relaxation in FeB amorphous system simulated by moleculular dynamics. J. Non-Cryst. Solids 1993, 159, 80– 87,  DOI: 10.1016/0022-3093(93)91284-A
    Google Scholar
    There is no corresponding record for this reference.
  61. 61
    Ong, S. P.; Richards, W. D.; Jain, A.; Hautier, G.; Kocher, M.; Cholia, S.; Gunter, D.; Chevrier, V. L.; Persson, K. A.; Ceder, G. Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis. Comput. Mater. Sci. 2013, 68, 314– 319,  DOI: 10.1016/j.commatsci.2012.10.028
    Google Scholar
    61
    Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
    Ong, Shyue Ping; Richards, William Davidson; Jain, Anubhav; Hautier, Geoffroy; Kocher, Michael; Cholia, Shreyas; Gunter, Dan; Chevrier, Vincent L.; Persson, Kristin A.; Ceder, Gerbrand
    Computational Materials Science (2013), 68 (), 314-319CODEN: CMMSEM; ISSN:0927-0256. (Elsevier B.V.)
    We present the Python Materials Genomics (pymatgen) library, a robust, open-source Python library for materials anal. A key enabler in high-throughput computational materials science efforts is a robust set of software tools to perform initial setup for the calcns. (e.g., generation of structures and necessary input files) and post-calcn. anal. to derive useful material properties from raw calcd. data. The pymatgen library aims to meet these needs by (1) defining core Python objects for materials data representation, (2) providing a well-tested set of structure and thermodn. analyses relevant to many applications, and (3) establishing an open platform for researchers to collaboratively develop sophisticated analyses of materials data obtained both from first principles calcns. and expts. The pymatgen library also provides convenient tools to obtain useful materials data via the Materials Project's REpresentational State Transfer (REST) Application Programming Interface (API). As an example, using pymatgen's interface to the Materials Project's RESTful API and phase diagram package, we demonstrate how the phase and electrochem. stability of a recently synthesized material, Li4SnS4, can be analyzed using a min. of computing resources. We find that Li4SnS4 is a stable phase in the Li-Sn-S phase diagram (consistent with the fact that it can be synthesized), but the narrow range of lithium chem. potentials for which it is predicted to be stable would suggest that it is not intrinsically stable against typical electrodes used in lithium-ion batteries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsVGjt7g%253D&md5=104f567dbd8f4199911ded91bc42100e
  62. 62
    Pannikkat, A.; Raj, R. Measurement of an electrical potential induced by normal stress applied to the interface of an ionic material at elevated temperatures. Acta Mater. 1999, 47, 3423– 3431,  DOI: 10.1016/S1359-6454(99)00206-2
    Google Scholar
    62
    Measurement of an electrical potential induced by normal stress applied to the interface of an ionic material at elevated temperatures
    Pannikkat, A. K.; Raj, R.
    Acta Materialia (1999), 47 (12), 3423-3431CODEN: ACMAFD; ISSN:1359-6454. (Elsevier Science Ltd.)
    The measurement of a p.d. between 2 surfaces of ZrO2 is reported, when a normal stress is applied to one surface, leaving the other surface stress free. The p.d. is proportional to the applied stress over a wide range. The proportionality const. represents a new thermodn. measurement of the interfacial state because the measurement is reversible and independent of temp. In ZrO2, the proportionality const. is related to the vol. and the charge on the O ion by considering thermodn. equil. among the electrochem. potentials of the O ion at the stressed and unstressed interfaces. The agreement with theory is within 10% for specimens made of polycryst. ZrO2, or single crystal cubic ZrO2 of (100) orientation. The proportionality const. changes by up to 20% for other orientations of the single crystal; this change is attributed to differences in the effective charge on the O ion on different surface orientations. The kinetics of the voltage response was also investigated in detail; it is consistent with the diffusion of the O ion along the interface formed between the metal electrode and the ZrO2 surface. The present measurements provide the first exptl. confirmation of the fundamental relationship between the chem. potential, the normal traction, and the at. vol. of species at interfaces of cryst. materials. The measurement has implications in further understanding of diffusional creep, creep cavitation, and sintering in ionic (or partially ionic) solids.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1MXmvVaku78%253D&md5=929fe736a48797d25a19d934c8079ebf
  63. 63
    Xie, T.; Grossman, J. C. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Phys. Rev. Lett. 2018, 120, 145301,  DOI: 10.1103/PhysRevLett.120.145301
    Google Scholar
    63
    Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
    Xie, Tian; Grossman, Jeffrey C.
    Physical Review Letters (2018), 120 (14), 145301CODEN: PRLTAO; ISSN:1079-7114. (American Physical Society)
    The use of machine learning methods for accelerating the design of cryst. materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chem. insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of cryst. materials. Our method provides a highly accurate prediction of d. functional theory calcd. properties for eight different properties of crystals with various structure types and compns. after being trained with 104 data points. Further, our framework is interpretable because one can ext. the contributions from local chem. environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXltFSnu7c%253D&md5=93beb5675af86cf95e07c82c136f3511
  64. 64
    Jain, A.; Ong, S. P.; Hautier, G.; Chen, W.; Richards, W. D.; Dacek, S.; Cholia, S.; Gunter, D.; Skinner, D.; Ceder, G.; Persson, K. A. The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater. 2013, 1, 011002,  DOI: 10.1063/1.4812323
    Google Scholar
    64
    Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
    Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy; Chen, Wei; Richards, William Davidson; Dacek, Stephen; Cholia, Shreyas; Gunter, Dan; Skinner, David; Ceder, Gerbrand; Persson, Kristin A.
    APL Materials (2013), 1 (1), 011002/1-011002/11CODEN: AMPADS; ISSN:2166-532X. (American Institute of Physics)
    Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorg. materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform rapid-prototyping'' of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design. (c) 2013 American Institute of Physics.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtlyktLjF&md5=88cb8642abed05e6b34a2191519b3ff3
  65. 65
    de Jong, M.; Chen, W.; Angsten, T.; Jain, A.; Notestine, R.; Gamst, A.; Sluiter, M.; Krishna Ande, C.; van der Zwaag, S.; Plata, J. J.; Toher, C.; Curtarolo, S.; Ceder, G.; Persson, K. A.; Asta, M. Charting the complete elastic properties of inorganic crystalline compounds. Sci. Data 2015, 2, 150009,  DOI: 10.1038/sdata.2015.9
    Google Scholar
    65
    Charting the complete elastic properties of inorganic crystalline compounds
    de Jong, Maarten; Chen, Wei; Angsten, Thomas; Jain, Anubhav; Notestine, Randy; Gamst, Anthony; Sluiter, Marcel; Krishna Ande, Chaitanya; van der Zwaag, Sybrand; Plata, Jose J.; Toher, Cormac; Curtarolo, Stefano; Ceder, Gerbrand; Persson, Kristin A.; Asta, Mark
    Scientific Data (2015), 2 (), 150009CODEN: SDCABS; ISSN:2052-4463. (Nature Publishing Group)
    The elastic const. tensor of an inorg. compd. provides a complete description of the response of the material to external stresses in the elastic limit. It thus provides fundamental insight into the nature of the bonding in the material, and it is known to correlate with many mech. properties. Despite the importance of the elastic const. tensor, it has been measured for a very small fraction of all known inorg. compds., a situation that limits the ability of materials scientists to develop new materials with targeted mech. responses. To address this deficiency, we present here the largest database of calcd. elastic properties for inorg. compds. to date. The database currently contains full elastic information for 1,181 inorg. compds., and this no. is growing steadily. The methods used to develop the database are described, as are results of tests that establish the accuracy of the data. In addn., we document the database format and describe the different ways it can be accessed and analyzed in efforts related to materials discovery and design.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXoslOku74%253D&md5=0479a0c29355429904fd5fe14ca08cd8
  66. 66
    Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 1996, 77, 3865,  DOI: 10.1103/PhysRevLett.77.3865
    Google Scholar
    66
    Generalized gradient approximation made simple
    Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias
    Physical Review Letters (1996), 77 (18), 3865-3868CODEN: PRLTAO; ISSN:0031-9007. (American Physical Society)
    Generalized gradient approxns. (GGA's) for the exchange-correlation energy improve upon the local spin d. (LSD) description of atoms, mols., and solids. We present a simple derivation of a simple GGA, in which all parameters (other than those in LSD) are fundamental consts. Only general features of the detailed construction underlying the Perdew-Wang 1991 (PW91) GGA are invoked. Improvements over PW91 include an accurate description of the linear response of the uniform electron gas, correct behavior under uniform scaling, and a smoother potential.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK28XmsVCgsbs%253D&md5=55943538406ee74f93aabdf882cd4630
  67. 67
    Paszke, A.; Gross, S.; Chintala, S.; Chanan, G.; Yang, E.; DeVito, Z.; Lin, Z.; Desmaison, A.; Antiga, L.; Lerer, A. Automatic differentiation in PyTorch , NIPS-W, 2017.
    Google Scholar
    There is no corresponding record for this reference.
  68. 68
    Tikekar, M. D.; Archer, L. A.; Koch, D. L. Stabilizing electrodeposition in elastic solid electrolytes containing immobilized anions. Sci. Adv. 2016, 2, 1600320,  DOI: 10.1126/sciadv.1600320
    Google Scholar
    68
    Stabilizing electrodeposition in elastic solid electrolytes containing immobilized anions
    Tikekar, Mukul D.; Archer, Lynden A.; Koch, Donald L.
    Science Advances (2016), 2 (7), e1600320/1-e1600320/15CODEN: SACDAF; ISSN:2375-2548. (American Association for the Advancement of Science)
    Ion transport - driven instabilities in electrodeposition of metals that lead to morphol. instabilities and dendrites are receiving renewed attention because mitigation strategies are needed for improving recharge-ability and safety of lithium batteries. The growth rate of these morphol. instabilities can be slowed by immobilizing a fraction of anions within the electrolyte to reduce the elec. field at the metal electrode. We analyze the role of elastic deformation of the solid electrolyte with immobilized anions and present theory combining the roles of separator elasticity and modified transport to evaluate the factors affecting the stability of planar deposition over a wide range of current densities. We find that stable electrodeposition can be easily achieved even at relatively high current densities in electrolytes/separators with moderate polymer-like mech. moduli, provided a small fraction of anions are immobilized in the separator.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXkslKrtLo%253D&md5=63591374b78e8f147b116ede066d3fa7
  69. 69
    Seino, Y.; Ota, T.; Takada, K.; Hayashi, A.; Tatsumisago, M. A sulphide lithium super ion conductor is superior to liquid ion conductors for use in rechargeable batteries. Energy Environ. Sci. 2014, 7, 627– 631,  DOI: 10.1039/C3EE41655K
    Google Scholar
    69
    A sulphide lithium super ion conductor is superior to liquid ion conductors for use in rechargeable batteries
    Seino, Yoshikatsu; Ota, Tsuyoshi; Takada, Kazunori; Hayashi, Akitoshi; Tatsumisago, Masahiro
    Energy & Environmental Science (2014), 7 (2), 627-631CODEN: EESNBY; ISSN:1754-5706. (Royal Society of Chemistry)
    We report that a heat-treated Li2S-P2S5 glass-ceramic conductor has an extremely high ionic cond. of 1.7 × 10-2 S cm-1 and the lowest conduction activation energy of 17 kJ mol-1 at room temp. among lithium-ion conductors reported to date. The optimum conditions of the heat treatment reduce the grain boundary resistance, and the influence of voids, to increase the Li+ ionic cond. of the solid electrolyte so that it is greater than the conductivities of liq. electrolytes, when the transport no. of lithium ions in the inorg. electrolyte is unity.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsFakt7Y%253D&md5=bdd225e1bf4147f70baf646869b2c4f7
  70. 70
    Li, Y.; Li, Y.; Pei, A.; Yan, K.; Sun, Y.; Wu, C.-L.; Joubert, L.-M.; Chin, R.; Koh, A. L.; Yu, Y.; Perrino, J.; Butz, B.; Chu, S.; Cui, Y. Atomic structure of sensitive battery materials and interfaces revealed by cryo–electron microscopy. Science 2017, 358, 506– 510,  DOI: 10.1126/science.aam6014
    Google Scholar
    70
    Atomic structure of sensitive battery materials and interfaces revealed by cryo-electron microscopy
    Li, Yuzhang; Li, Yanbin; Pei, Allen; Yan, Kai; Sun, Yongming; Wu, Chun-Lan; Joubert, Lydia-Marie; Chin, Richard; Koh, Ai Leen; Yu, Yi; Perrino, John; Butz, Benjamin; Chu, Steven; Cui, Yi
    Science (Washington, DC, United States) (2017), 358 (6362), 506-510CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
    Whereas std. transmission electron microscopy studies are unable to preserve the native state of chem. reactive and beam-sensitive battery materials after operation, such materials remain pristine at cryogenic conditions. It is then possible to atomically resolve individual Li metal atoms and their interface with the solid electrolyte interphase (SEI). We observe that dendrites in carbonate-based electrolytes grow along the <111> (preferred), <110>, or <211> directions as faceted, single-cryst. nanowires. These growth directions can change at kinks with no observable crystallog. defect. We reveal distinct SEI nanostructures formed in different electrolytes.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhslSgsb7O&md5=313b19162d2034ecf59cdef499b39565
  71. 71
    Stroh, A. N. Dislocations and Cracks in Anisotropic Elasticity. Philos. Mag. 1958, 3, 625– 646,  DOI: 10.1080/14786435808565804
    Google Scholar
    71
    Dislocations and cracks in anisotropic elasticity
    Stroh, A. N.
    Philosophical Magazine (1798-1977) (1958), 3 (), 625-46CODEN: PHMAA4; ISSN:0031-8086.
    The solution of the elastic equations is considered for the case in which the state of the solid is independent of 1 of the 3 Cartesian co.ovrddot.ordinates. The stresses due to a dislocation, a wall of parallel dislocations, and a crack in an arbitrary nonuniform stress field are obtained. The results hold for the most general anisotropy in which no symmetry elements of the crystal are assumed.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaG1MXitV2ltQ%253D%253D&md5=4195bf838814988673bec6632a6b1dc8
  72. 72
    Stroh, A. N. Steady State Problems in Anisotropic Elasticity. J. Math. Phys. 1962, 41, 77– 103,  DOI: 10.1002/sapm196241177
    Google Scholar
    There is no corresponding record for this reference.
  73. 73
    Hall, S. R.; Allen, F. H.; Brown, I. D. The crystallographic information file (CIF): a new standard archive file for crystallography. Acta Crystallogr., Sect. A: Found. Crystallogr. 1991, 47, 655– 685,  DOI: 10.1107/S010876739101067X
    Google Scholar
    There is no corresponding record for this reference.
  74. 74
    Mouhat, F.; Coudert, F. m. c.-X. Necessary and sufficient elastic stability conditions in various crystal systems. Phys. Rev. B: Condens. Matter Mater. Phys. 2014, 90, 224104,  DOI: 10.1103/PhysRevB.90.224104
    Google Scholar
    74
    Necessary and sufficient elastic stability conditions in various crystal systems
    Mouhat, Felix; Coudert, Francois-Xavier
    Physical Review B: Condensed Matter and Materials Physics (2014), 90 (22), 224104/1-224104/4, 4 pp.CODEN: PRBMDO; ISSN:1098-0121. (American Physical Society)
    While the Born elastic stability criteria are well known for cubic crystals, there is some confusion in the literature about the form they should take for lower-symmetry crystal classes. Here we present closed form necessary and sufficient conditions for elastic stability in all crystal classes, as a concise and pedagogical ref. to stability criteria in noncubic materials.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXivVGkur0%253D&md5=d8f5d43f831b6e45962e567bee94a008
  75. 75
    Ahmad, Z.; Viswanathan, V. Quantification of uncertainty in first-principles predicted mechanical properties of solids: Application to solid ion conductors. Phys. Rev. B: Condens. Matter Mater. Phys. 2016, 94, 064105,  DOI: 10.1103/PhysRevB.94.064105
    Google Scholar
    There is no corresponding record for this reference.
  76. 76
    Friedman, J. H. Greedy Function Approximation: A Gradient Boosting Machine. Ann. Stat. 2001, 29, 1189– 1232,  DOI: 10.1214/aos/1013203451
    Google Scholar
    There is no corresponding record for this reference.
  77. 77
    Friedman, J. H.; Hastie, T.; Tibshirani, R. The elements of statistical learning; Springer series in statistics: New York, 2001; Vol. 1.
    Google Scholar
    There is no corresponding record for this reference.
  78. 78
    Freund, Y.; Schapire, R. E. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. J. Comput. Syst. Sci. 1997, 55, 119– 139,  DOI: 10.1006/jcss.1997.1504
    Google Scholar
    There is no corresponding record for this reference.
  79. 79
    Drucker, H. Improving Regressors Using Boosting Techniques , Proceedings of the Fourteenth International Conference on Machine Learning, San Francisco, CA, United States, 1997; pp 107– 115.
    Google Scholar
    There is no corresponding record for this reference.
  80. 80
    Smola, A. J.; Schülkopf, B. A tutorial on support vector regression. Statistics and Computing 2004, 14, 199– 222,  DOI: 10.1023/B:STCO.0000035301.49549.88
    Google Scholar
    There is no corresponding record for this reference.
  81. 81
    MacKay, D. J. C. Bayesian Interpolation. Neural Comput. 1992, 4, 415– 447,  DOI: 10.1162/neco.1992.4.3.415
    Google Scholar
    There is no corresponding record for this reference.
  82. 82
    Pedregosa, F. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825– 2830
    Google Scholar
    There is no corresponding record for this reference.
  83. 83
    Deng, Z.; Wang, Z.; Chu, I.-H.; Luo, J.; Ong, S. P. Elastic Properties of Alkali Superionic Conductor Electrolytes from First Principles Calculations. J. Electrochem. Soc. 2016, 163, A67– A74,  DOI: 10.1149/2.0061602jes
    Google Scholar
    83
    Elastic Properties of Alkali Superionic Conductor Electrolytes from First Principles Calculations
    Deng, Zhi; Wang, Zhenbin; Chu, Iek-Heng; Luo, Jian; Ong, Shyue Ping
    Journal of the Electrochemical Society (2016), 163 (2), A67-A74CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
    In this work, we present a comprehensive investigation of the elastic properties (the full elastic tensor, bulk, shear and Young's moduli, and Poisson's ratio) of 23 well-known ceramic alkali superionic conductor electrolytes (SICEs) using first principles calcns. We find that the computed elastic moduli are in good agreement with exptl. data (wherever available) and chem. bonding nature. The anion species and structural framework have a significant influence on the elastic properties, and the relative elastic moduli of the various classes of SICEs follow the order thiophosphate < antiperovskite < phosphate < NASICON < garnet < perovskite. Within the same framework structure, we observe that Na SICEs are softer than their Li analogs. We discuss the implications of these findings in the context of fabrication, battery operation, and enabling a Li metal anode. The data computed in this work will also serve as a useful ref. for future expts. as well as theor. modeling of SICEs for rechargeable alkali-ion batteries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhvFylurvI&md5=b8172c27880503a159ab9fe8a1a7c6c3
  84. 84
    Ranganathan, S. I.; Ostoja-Starzewski, M. Universal Elastic Anisotropy Index. Phys. Rev. Lett. 2008, 101, 055504,  DOI: 10.1103/PhysRevLett.101.055504
    Google Scholar
    84
    Universal elastic anisotropy index
    Ranganathan, Shivakumar I.; Ostoja-Starzewski, Martin
    Physical Review Letters (2008), 101 (5), 055504/1-055504/4CODEN: PRLTAO; ISSN:0031-9007. (American Physical Society)
    Practically all elastic single crystals are anisotropic, which calls for an appropriate universal measure to quantify the extent of anisotropy. A review of the existing anisotropy measures in the literature leads to a conclusion that they lack universality in the sense that they are nonunique and ignore contributions from the bulk part of the elastic stiffness (or compliance) tensor. Proceeding from extremal principles of elasticity, the authors introduce a new universal anisotropy index that overcomes the above limitations. Also, the authors establish special relations between the proposed anisotropy index and the existing anisotropy measures for special cases. A new elastic anisotropy diagram is constructed for over 100 different crystals (from cubic through triclinic), demonstrating that the proposed anisotropy measure is applicable to all types of elastic single crystals, and thus fills an important void in the existing literature.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhtVSju7nN&md5=40a1e4eb5d42153f25575b60665ccab9
  85. 85
    Lu, Z.; Ciucci, F. Metal Borohydrides as Electrolytes for Solid-State Li, Na, Mg, and Ca Batteries: A First-Principles Study. Chem. Mater. 2017, 29, 9308– 9319,  DOI: 10.1021/acs.chemmater.7b03284
    Google Scholar
    85
    Metal Borohydrides as Electrolytes for Solid-State Li, Na, Mg, and Ca Batteries: A First-Principles Study
    Lu, Ziheng; Ciucci, Francesco
    Chemistry of Materials (2017), 29 (21), 9308-9319CODEN: CMATEX; ISSN:0897-4756. (American Chemical Society)
    Metal borohydrides are a family of materials that were recently discovered to have extraordinary ionic conductivities, making them promising candidates as electrolytes for solid-state batteries (SSBs). In fact, various groups have measured the ionic conductivities and assembled batteries using specific borohydrides. However, there are no comprehensive studies assessing the thermodn. properties or discussing the suitability of metal borohydrides as electrolytes in SSBs, esp. for beyond-lithium applications. In this work, we investigate the electrochem. stability, interfacial characteristics, mech. properties, and ionic conductivities of Li, Na, Ca, and Mg borohydrides using first-principles calcns. Our results suggest that Li and Na borohydrides are unstable at high voltages. However, the corresponding decompn. products, i.e., B12H122--contg. phases, have wide electrochem. windows which protect the electrolyte, leading to large electrochem. windows as wide as 5 V. In addn., our simulations indicate that metal borohydrides are ductile, suggesting facile processing. However, their low shear moduli may result in metal dendrite formation. For Ca and Mg borohydrides, while they possess reasonably good electrochem. stability, the low cationic diffusivity may impede their practical use. Finally, the anion rotation barrier was shown to correlate with the superionic phase transition temp., suggesting that anion mixing may be a potential approach to achieve room-temp. superionic cond.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhs1Sks77I&md5=4f24aceeeec626b708e8a2546488d8c9
  86. 86
    Varley, J. B.; Kweon, K.; Mehta, P.; Shea, P.; Heo, T. W.; Udovic, T. J.; Stavila, V.; Wood, B. C. Understanding Ionic Conductivity Trends in Polyborane Solid Electrolytes from Ab Initio Molecular Dynamics. ACS Energy Lett. 2017, 2, 250– 255,  DOI: 10.1021/acsenergylett.6b00620
    Google Scholar
    86
    Understanding Ionic Conductivity Trends in Polyborane Solid Electrolytes from Ab Initio Molecular Dynamics
    Varley, Joel B.; Kweon, Kyoung; Mehta, Prateek; Shea, Patrick; Heo, Tae Wook; Udovic, Terrence J.; Stavila, Vitalie; Wood, Brandon C.
    ACS Energy Letters (2017), 2 (1), 250-255CODEN: AELCCP; ISSN:2380-8195. (American Chemical Society)
    Polyborane salts based on B12H122-, B10H102-, CB11H12-, and CB9H10- demonstrate high Li and Na superionic cond. that makes them attractive as electrolytes in all-solid-state batteries. Their chem. and structural diversity creates a versatile design space that could be used to optimize materials with higher cond. at lower temps.; however, many mechanistic details remain enigmatic, including reasons why certain known modifications lead to improved performance. We use extensive ab initio mol. dynamics simulations to explore the dependence of ionic cond. on cation/anion pair combinations for Li and Na polyborane salts. Further simulations are used to probe the influence of local modifications to chem., stoichiometry, and compn. Carbon doping, anion alloying, and cation off-stoichiometry are found to favorably introduce intrinsic disorder, facilitating local deviation from the expected cation population. Lattice expansion likewise has a pos. effect by aiding anion reorientations that are crit. for conduction. Implications for engineering polyboranes for improved ionic cond. are discussed.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XitFektb3P&md5=10bc784c32c1869968e8c1ae8ab49401
  87. 87
    Tang, W. S.; Matsuo, M.; Wu, H.; Stavila, V.; Zhou, W.; Talin, A. A.; Soloninin, A. V.; Skoryunov, R. V.; Babanova, O. A.; Skripov, A. V.; Unemoto, A.; Orimo, S.; Udovic, T. J. Liquid-Like Ionic Conduction in Solid Lithium and Sodium Monocarba-closo-Decaborates Near or at Room Temperature. Adv. Energy Mater. 2016, 6, 1502237,  DOI: 10.1002/aenm.201502237
    Google Scholar
    There is no corresponding record for this reference.
  88. 88
    Tang, W. S.; Unemoto, A.; Zhou, W.; Stavila, V.; Matsuo, M.; Wu, H.; Orimo, S.-i.; Udovic, T. J. Unparalleled lithium and sodium superionic conduction in solid electrolytes with large monovalent cage-like anions. Energy Environ. Sci. 2015, 8, 3637– 3645,  DOI: 10.1039/C5EE02941D
    Google Scholar
    88
    Unparalleled lithium and sodium superionic conduction in solid electrolytes with large monovalent cage-like anions
    Tang, Wan Si; Unemoto, Atsushi; Zhou, Wei; Stavila, Vitalie; Matsuo, Motoaki; Wu, Hui; Orimo, Shin-ichi; Udovic, Terrence J.
    Energy & Environmental Science (2015), 8 (12), 3637-3645CODEN: EESNBY; ISSN:1754-5706. (Royal Society of Chemistry)
    Solid electrolytes with sufficiently high conductivities and stabilities are the elusive answer to the inherent shortcomings of org. liq. electrolytes prevalent in today's rechargeable batteries. We recently revealed a novel fast-ion-conducting sodium salt, Na2B12H12, which contains large, icosahedral, divalent B12H122- anions that enable impressive superionic cond., albeit only above its 529 K phase transition. Its lithium congener, Li2B12H12, possesses an even more technol. prohibitive transition temp. above 600 K. Here we show that the chem. related LiCB11H12 and NaCB11H12 salts, which contain icosahedral, monovalent CB11H12- anions, both exhibit much lower transition temps. near 400 K and 380 K, resp., and truly stellar ionic conductivities (>0.1 S cm-1) unmatched by any other known polycryst. materials at these temps. With proper modifications, we are confident that room-temp.-stabilized superionic salts incorporating such large polyhedral anion building blocks are attainable, thus enhancing their future prospects as practical electrolyte materials in next-generation, all-solid-state batteries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhs1amsr%252FL&md5=6ce33938ab3579da7ee531eb41f73964
  89. 89
    Malmgren, S.; Ciosek, K.; Lindblad, R.; Plogmaker, S.; Kühn, J.; Rensmo, H.; Edström, K.; Hahlin, M. Consequences of air exposure on the lithiated graphite SEI. Electrochim. Acta 2013, 105, 83– 91,  DOI: 10.1016/j.electacta.2013.04.118
    Google Scholar
    89
    Consequences of air exposure on the lithiated graphite SEI
    Malmgren, Sara; Ciosek, Katarzyna; Lindblad, Rebecka; Plogmaker, Stefan; Kuhn, Julius; Rensmo, Haakan; Edstroem, Kristina; Hahlin, Maria
    Electrochimica Acta (2013), 105 (), 83-91CODEN: ELCAAV; ISSN:0013-4686. (Elsevier Ltd.)
    Consequences of air exposure on the surface compn. of one of the most reactive Li-ion battery components, the lithiated graphite, was studied using 280-835 eV soft XPS (SOXPES) as well as 1486.7 eV XPS (∼2 and ∼10 nm probing depth, resp.). Different depth regions of the solid electrolyte interphase (SEI) of graphite cycled vs. LiFePO4 were thereby examd. Also, the air sensitivity of samples subject to four different combinations of pre-treatments (washed/unwashed and exposed to air before or after vacuum treatment) was explored. The samples showed important changes after exposure to air, which are largely dependent on sample pre-treatment. Changes after exposure of unwashed samples exposed before vacuum treatment were attributed to reactions involving volatile species. On washed, air exposed samples, as well as unwashed samples exposed after vacuum treatment, effects attributed to LiOH formation in the innermost SEI were obsd. and suggested to be assocd. with partial delithiation of the surface region of the lithiated graphite electrode. Also, effects that can be attributed to LiPF6 decompn. were obsd. However, these effects were less pronounced than those attributed to reactions involving solvent species and the lithiated graphite.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtVehsbvE&md5=6475c40a670e813d2a8de45c21003e64
  90. 90
    Tasaki, K.; Goldberg, A.; Lian, J.-J.; Walker, M.; Timmons, A.; Harris, S. Solubility of Lithium Salts Formed on the Lithium-Ion Battery Negative Electrode Surface in Organic Solvents. J. Electrochem. Soc. 2009, 156, A1019– A1027,  DOI: 10.1149/1.3239850
    Google Scholar
    90
    Solubility of Lithium Salts Formed on the Lithium-Ion Battery Negative Electrode Surface in Organic Solvents
    Tasaki, Ken; Goldberg, Alex; Lian, Jian-Jie; Walker, Merry; Timmons, Adam; Harris, Stephen J.
    Journal of the Electrochemical Society (2009), 156 (12), A1019-A1027CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
    The soly. of lithium salts in di-Me carbonate (DMC) found in solid electrolyte interface films was detd. The salt-DMC solns. were evapd., and the salts were transferred into water for ion cond. measurements. The salts examd. included Li2CO3, lithium oxalate [(LiCO2)2], LiF, LiOH, lithium Me carbonate (LiOCO2CH3), and lithium Et carbonate (LiOCO2C2H5). The salt molarity in DMC ranged from 9.6 × 10-4 mol/L (LiOCO2CH3) to 9 × 10-5 mol/L (Li2CO3) in the order of LiOCO2CH3 > LiOCO2C2H5 > LiOH > LiF > (LiCO2)2 > Li2CO3. XPS measurements on solid electrolyte interface films on the surface of the anode taken from a com. battery after soaking in DMC for 1 h suggested that the films can dissolve. Sep., the heat of dissoln. of the salts was calcd. from computer simulations for the same salts, including Li2O, lithium methoxide (LiOCH3), and dilithium ethylene glycol dicarbonate [(CH2OCO2Li)2:LiEDC] in both DMC and ethylene carbonate. The results from the computer simulations suggested that the order in which the salt was likely to dissolve in both DMC and ethylene carbonate was LiEDC > LiOCO2CH3 > LiOH > LiOCO2C2H5 > LiOCH3 > LiF > (LiCO2)2 > Li2CO3 > Li2O. This order agreed with the expt. in DMC within the exptl. error. Both expt. and computer simulations showed that the org. salts are more likely to dissolve in DMC than the inorg. salts. The calcns. also predicted that the salts dissolve more likely in ethylene carbonate than in DMC, in general. Moreover, the results from the study were used to discuss the capacity fading mechanism during the storage of lithium-ion batteries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtlCmsbzN&md5=433ac8ee99379a475c1e174d35423704
  91. 91
    Matsuo, M.; Nakamori, Y.; ichi Orimo, S.; Maekawa, H.; Takamura, H. Lithium superionic conduction in lithium borohydride accompanied by structural transition. Appl. Phys. Lett. 2007, 91, 224103,  DOI: 10.1063/1.2817934
    Google Scholar
    91
    Lithium superionic conduction in lithium borohydride accompanied by structural transition
    Matsuo, Motoaki; Nakamori, Yuko; Orimo, Shin-ichi; Maekawa, Hideki; Takamura, Hitoshi
    Applied Physics Letters (2007), 91 (22), 224103/1-224103/3CODEN: APPLAB; ISSN:0003-6951. (American Institute of Physics)
    The elec. cond. of LiBH4 measured by a.c. complex impedance increased by 3 orders of magnitude due to structural transition from orthorhombic to hexagonal at ∼390 K. The hexagonal phase exhibited a high elec. cond. of about 10-3 S/cm. The cond. calcd. from the Nernst-Einstein equation using the correlation time obtained from 7Li NMR agreed with the measured elec. cond. The elec. cond. in the hexagonal phase is due to Li superionic conduction.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhsVequ7zJ&md5=8949f5d9d2da1f61bf6ad230dac90749
  92. 92
    Johnson, R.; Biefeld, R.; Keck, J. Ionic conductivity in Li5AlO4 and LiOH. Mater. Res. Bull. 1977, 12, 577– 587,  DOI: 10.1016/0025-5408(77)90066-6
    Google Scholar
    92
    Ionic conductivity in lithium aluminum oxide (Li5AlO4) and lithium hydroxide
    Johnson, R. T., Jr.; Biefeld, R. M.; Keck, J. D.
    Materials Research Bulletin (1977), 12 (6), 577-87CODEN: MRBUAC; ISSN:0025-5408.
    The ionic cond. and thermal properties of Li5AlO4 and LiOH were measured in wet and dry environments. An endothehrmic reaction at ∼415°C and an assocd. large increase in cond. were obsd. both in Li5AlO4, in wet environment, and in LiOH. The large cond. increase in Li5AlO4 results from LiOH retained within the material. The reaction(s) for formation of LiOH within Li5AlO4 ad the assocd. elec. changes appear to be reversible as the environment switches from wet to dry at high temps. There is a significant (>1%) electronic contribution to the cond. in these materials.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaE2sXkslKqs7k%253D&md5=49b9b53cd2c56638c6e2539226cf0764
  93. 93
    Maekawa, H.; Matsuo, M.; Takamura, H.; Ando, M.; Noda, Y.; Karahashi, T.; Orimo, S.-i. Halide-Stabilized LiBH4, a Room-Temperature Lithium Fast-Ion Conductor. J. Am. Chem. Soc. 2009, 131, 894– 895,  DOI: 10.1021/ja807392k
    Google Scholar
    93
    Halide-Stabilized LiBH4, a Room-Temperature Lithium Fast-Ion Conductor
    Maekawa, Hideki; Matsuo, Motoaki; Takamura, Hitoshi; Ando, Mariko; Noda, Yasuto; Karahashi, Taiki; Orimo, Shin-ichi
    Journal of the American Chemical Society (2009), 131 (3), 894-895CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
    A review on development of lithium superionic conductors based on LiBH4 and lithium halides. Using these compds., room-temp. high lithium ion cond. was imparted to a hydride system that had not been considered a lithium ion electrolyte. The electrochem. measurements showed a great advantage of this material as an extremely lightwt. lithium electrolyte for high energy d. batteries. Versatile properties of these materials make them suitable for use in all-solid-state batteries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXisValtQ%253D%253D&md5=7602a4b2a0f259ac649e6ce4dfe8e53b
  94. 94
    Das, S.; Ngene, P.; Norby, P.; Vegge, T.; de Jongh, P. E.; Blanchard, D. All-Solid-State Lithium-Sulfur Battery Based on a Nanoconfined LiBH4 Electrolyte. J. Electrochem. Soc. 2016, 163, A2029– A2034,  DOI: 10.1149/2.0771609jes
    Google Scholar
    94
    All-Solid-State Lithium-Sulfur Battery Based on a Nanoconfined LiBH4 Electrolyte
    Das, Supti; Ngene, Peter; Norby, Poul; Vegge, Tejs; de Jongh, Petra E.; Blanchard, Didier
    Journal of the Electrochemical Society (2016), 163 (9), A2029-A2034CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
    In this work we characterize all-solid-state lithium-sulfur batteries based on nano-confined LiBH4 in mesoporous silica as solid electrolytes. The nano-confined LiBH4 has fast ionic lithium cond. at room temp., 0.1 mScm-1, negligible electronic cond. and its cationic transport no. (t+ = 0.96), close to unity, demonstrates a purely cationic conductor. The electrolyte has an excellent stability against lithium metal. The behavior of the batteries is studied by cyclic voltammetry and repeated charge/discharge cycles in galvanostatic conditions. The batteries show good performance, delivering high capacities vs. sulfur mass, typically 1220 mAhg-1 after 40 cycles at moderate temp. (55°), 0.03 C rates and working voltage of 2 V.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xht1Krsr%252FP&md5=ee1106f5cdf799cd3505e1078501696f
  95. 95
    Morales-García, A.; Valero, R.; Illas, F. An Empirical, yet Practical Way To Predict the Band Gap in Solids by Using Density Functional Band Structure Calculations. J. Phys. Chem. C 2017, 121, 18862– 18866,  DOI: 10.1021/acs.jpcc.7b07421
    Google Scholar
    95
    An empirical, yet practical way to predict the band gap in solids by using density functional band structure calculations
    Morales-Garcia, Angel; Valero, Rosendo; Illas, Francesc
    Journal of Physical Chemistry C (2017), 121 (34), 18862-18866CODEN: JPCCCK; ISSN:1932-7447. (American Chemical Society)
    Band structure calcns. based on d. functional theory (DFT) with local or gradient-cor. exchange-correlation potentials are known to severely underestimate the band gap of semiconducting and insulating materials. Alternative approaches have been proposed: from semiempirical setups, such as the so-called DFT+U, to hybrid d. functionals using a fraction of nonlocal Fock exchange, to modifications of semilocal d. functionals. However, the resulting methods appear to be material dependent and lack theor. rigor. The rigorous many-body perturbation theory based on GW methods provides accurate results but at a very high computational cost. Hereby, we show that a linear correlation between the electronic band gaps obtained from std. DFT and GW approaches exists for most materials and argue that (1) this is a strong indication that the problem of predicting band gaps from std. DFT calcn. arises from the assignment of a phys. meaning to the Kohn-Sham energy levels rather than from intrinsic errors of the DFT methods and (2) it provides a practical way to obtain GW-like quality results from std. DFT calcns. The latter will be esp. useful for systems where the unit cell involves a large no. of atoms as in the case of doped or defect-contg. materials for which GW calcns. become unfeasible.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXht12mtLnE&md5=b0a676344af3832e6ff75461b9232229
  96. 96
    Towns, J.; Cockerill, T.; Dahan, M.; Foster, I.; Gaither, K.; Grimshaw, A.; Hazlewood, V.; Lathrop, S.; Lifka, D.; Peterson, G. D.; Roskies, R.; Scott, J. R.; Wilkins-Diehr, N. XSEDE: Accelerating Scientific Discovery. Comput. Sci. Eng. 2014, 16, 62– 74,  DOI: 10.1109/MCSE.2014.80
    Google Scholar
    There is no corresponding record for this reference.
  97. 97
    Nystrom, N. A.; Levine, M. J.; Roskies, R. Z.; Scott, J. R. Bridges: A Uniquely Flexible HPC Resource for New Communities and Data Analytics. Proceedings of the 2015 XSEDE Conference: Scientific Achievements Enabled by Enhanced Cyberinfrastructure 2015, 1– 8,  DOI: 10.1145/2792745.2792775
    Google Scholar
    There is no corresponding record for this reference.

Cited By

Click to copy section linkSection link copied!
Citation Statements
Explore this article's citation statements on scite.ai
powered by  Scite

This article is cited by 207 publications.

  1. Linley Li Lin, Ramon Alvarez-Puebla, Luis M. Liz-Marzán, Matt Trau, Jing Wang, Laura Fabris, Xiang Wang, Guokun Liu, Shuping Xu, Xiao Xia Han, Liangbao Yang, Aiguo Shen, Shikuan Yang, Yikai Xu, Chunchun Li, Jinqing Huang, Shao-Chuang Liu, Jian-An Huang, Indrajit Srivastava, Ming Li, Limei Tian, Lam Bang Thanh Nguyen, Xinyuan Bi, Dana Cialla-May, Pavel Matousek, Nicholas Stone, Randy P. Carney, Wei Ji, Wei Song, Zhou Chen, In Yee Phang, Malou Henriksen-Lacey, Haoran Chen, Zongyu Wu, Heng Guo, Hao Ma, Gennadii Ustinov, Siheng Luo, Sara Mosca, Benjamin Gardner, Yi-Tao Long, Juergen Popp, Bin Ren, Shuming Nie, Bing Zhao, Xing Yi Ling, Jian Ye. Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges. ACS Applied Materials & Interfaces 2025, 17 (11) , 16287-16379. https://doi.org/10.1021/acsami.4c17502
  2. Souvik Manna, Poulami Paul, Surya Sekhar Manna, Sandeep Das, Biswarup Pathak. Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects. Chemistry of Materials 2025, 37 (5) , 1759-1787. https://doi.org/10.1021/acs.chemmater.4c03486
  3. Aashutosh Mistry, Hans-Georg Steinrück, Michael F. Toney, Nitash P. Balsara, Venkat Srinivasan. Multiple Operando Fields Can Identify a Predictive Mass Transport Theory in Electrolytes. The Journal of Physical Chemistry C 2025, 129 (6) , 2874-2882. https://doi.org/10.1021/acs.jpcc.4c06851
  4. Nicholas S. Grundish, Brandi Ransom, Austin D. Sendek, Lenson A. Pellouchoud, Yutao Li, Evan J. Reed. Predicting Anion Redox in Secondary Battery Cathode Materials with a Data-Driven Model. The Journal of Physical Chemistry C 2024, 128 (40) , 16844-16853. https://doi.org/10.1021/acs.jpcc.4c02079
  5. Stephen R. Xie, Shreyas J. Honrao, John W. Lawson. High-Throughput Screening of Li Solid-State Electrolytes with Bond Valence Methods and Machine Learning. Chemistry of Materials 2024, 36 (19) , 9320-9329. https://doi.org/10.1021/acs.chemmater.3c02841
  6. Eder G. Lomeli, Brandi Ransom, Akash Ramdas, Daniel Jost, Brian Moritz, Austin D. Sendek, Evan J. Reed, Thomas P. Devereaux. Predicting Reactivity and Passivation of Solid-State Battery Interfaces. ACS Applied Materials & Interfaces 2024, 16 (38) , 51584-51594. https://doi.org/10.1021/acsami.4c06095
  7. Md Salman Rabbi Limon, Zeeshan Ahmad. Heterogeneity in Point Defect Distribution and Mobility in Solid Ion Conductors. ACS Applied Materials & Interfaces 2024, 16 (38) , 50948-50960. https://doi.org/10.1021/acsami.4c12128
  8. Sooryadas Sudhakaran, T. K. Bijoy. A Comprehensive Review of Current and Emerging Binder Technologies for Energy Storage Applications. ACS Applied Energy Materials 2023, 6 (23) , 11773-11794. https://doi.org/10.1021/acsaem.3c02218
  9. Randy Jalem, Yoshitaka Tateyama, Kazunori Takada, Seong-Hoon Jang. Multiobjective Solid Electrolyte Design of Tetragonal and Cubic Inverse-Perovskites for All-Solid-State Lithium-Ion Batteries by High-Throughput Density Functional Theory Calculations and AI-Driven Methods. The Journal of Physical Chemistry C 2023, 127 (35) , 17307-17323. https://doi.org/10.1021/acs.jpcc.3c02801
  10. Honghao Chen, Yingzhe Zheng, Jiali Li, Lanyu Li, Xiaonan Wang. AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS). ACS Nano 2023, 17 (11) , 9763-9792. https://doi.org/10.1021/acsnano.3c01062
  11. Sheng Gong, Shuo Wang, Tian Xie, Woo Hyun Chae, Runze Liu, Yang Shao-Horn, Jeffrey C. Grossman. Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning. JACS Au 2022, 2 (9) , 1964-1977. https://doi.org/10.1021/jacsau.2c00235
  12. Sherif Abdulkader Tawfik, Phuoc Nguyen, Truyen Tran, Tiffany R. Walsh, Svetha Venkatesh. Machine Learning-Aided Exploration of Ultrahard Materials. The Journal of Physical Chemistry C 2022, 126 (37) , 15952-15961. https://doi.org/10.1021/acs.jpcc.2c03926
  13. Haoxin Mai, Tu C. Le, Dehong Chen, David A. Winkler, Rachel A. Caruso. Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery. Chemical Reviews 2022, 122 (16) , 13478-13515. https://doi.org/10.1021/acs.chemrev.2c00061
  14. Aashutosh Mistry, Zhou Yu, Brandon L. Peters, Chao Fang, Rui Wang, Larry A. Curtiss, Nitash P. Balsara, Lei Cheng, Venkat Srinivasan. Toward Bottom-Up Understanding of Transport in Concentrated Battery Electrolytes. ACS Central Science 2022, 8 (7) , 880-890. https://doi.org/10.1021/acscentsci.2c00348
  15. Zhuole Lu, Parvin Adeli, Chae-Ho Yim, Ming Jiang, Jacob Rempel, Zhi Wen Chen, Shwetank Yadav, Patrick Mercier, Yaser Abu-Lebdeh, Chandra Veer Singh. Automatically Capturing Key Features for Predicting Superionic Conductivity of Solid-State Electrolytes Using a Neural Network. ACS Applied Energy Materials 2022, 5 (7) , 8042-8048. https://doi.org/10.1021/acsaem.2c00493
  16. Teo Lombardo, Marc Duquesnoy, Hassna El-Bouysidy, Fabian Årén, Alfonso Gallo-Bueno, Peter Bjørn Jørgensen, Arghya Bhowmik, Arnaud Demortière, Elixabete Ayerbe, Francisco Alcaide, Marine Reynaud, Javier Carrasco, Alexis Grimaud, Chao Zhang, Tejs Vegge, Patrik Johansson, Alejandro A. Franco. Artificial Intelligence Applied to Battery Research: Hype or Reality?. Chemical Reviews 2022, 122 (12) , 10899-10969. https://doi.org/10.1021/acs.chemrev.1c00108
  17. Nan Yao, Xiang Chen, Zhong-Heng Fu, Qiang Zhang. Applying Classical, Ab Initio, and Machine-Learning Molecular Dynamics Simulations to the Liquid Electrolyte for Rechargeable Batteries. Chemical Reviews 2022, 122 (12) , 10970-11021. https://doi.org/10.1021/acs.chemrev.1c00904
  18. Tong Liu, Xinsheng Wu, Shang Zhu, Francesca Lorandi, Longchang Ni, Sipei Li, Mingkang Sun, Brian P. Bloom, David H. Waldeck, Venkatasubramanian Viswanathan, Jay F. Whitacre, Krzysztof Matyjaszewski. Polymer-Stabilized Liquid Metal Nanoparticles as a Scalable Current Collector Engineering Approach Enabling Lithium Metal Anodes. ACS Applied Energy Materials 2022, 5 (3) , 3615-3625. https://doi.org/10.1021/acsaem.1c04106
  19. Sheng Gong, Shuo Wang, Taishan Zhu, Xi Chen, Zhenze Yang, Markus J. Buehler, Yang Shao-Horn, Jeffrey C. Grossman. Screening and Understanding Li Adsorption on Two-Dimensional Metallic Materials by Learning Physics and Physics-Simplified Learning. JACS Au 2021, 1 (11) , 1904-1914. https://doi.org/10.1021/jacsau.1c00260
  20. Eunseong Choi, Junho Jo, Wonjin Kim, Kyoungmin Min. Searching for Mechanically Superior Solid-State Electrolytes in Li-Ion Batteries via Data-Driven Approaches. ACS Applied Materials & Interfaces 2021, 13 (36) , 42590-42597. https://doi.org/10.1021/acsami.1c07999
  21. Nicole S. Schauser, Gabrielle A. Kliegle, Piper Cooke, Rachel A. Segalman, Ram Seshadri. Database Creation, Visualization, and Statistical Learning for Polymer Li+-Electrolyte Design. Chemistry of Materials 2021, 33 (13) , 4863-4876. https://doi.org/10.1021/acs.chemmater.0c04767
  22. Aashutosh Mistry, Alejandro A. Franco, Samuel J. Cooper, Scott A. Roberts, Venkatasubramanian Viswanathan. How Machine Learning Will Revolutionize Electrochemical Sciences. ACS Energy Letters 2021, 6 (4) , 1422-1431. https://doi.org/10.1021/acsenergylett.1c00194
  23. Hieu A. Doan, Garvit Agarwal, Hai Qian, Michael J. Counihan, Joaquín Rodríguez-López, Jeffrey S. Moore, Rajeev S. Assary. Quantum Chemistry-Informed Active Learning to Accelerate the Design and Discovery of Sustainable Energy Storage Materials. Chemistry of Materials 2020, 32 (15) , 6338-6346. https://doi.org/10.1021/acs.chemmater.0c00768
  24. Anthony Yu-Tung Wang, Ryan J. Murdock, Steven K. Kauwe, Anton O. Oliynyk, Aleksander Gurlo, Jakoah Brgoch, Kristin A. Persson, Taylor D. Sparks. Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices. Chemistry of Materials 2020, 32 (12) , 4954-4965. https://doi.org/10.1021/acs.chemmater.0c01907
  25. Yanming Wang, Tian Xie, Arthur France-Lanord, Arthur Berkley, Jeremiah A. Johnson, Yang Shao-Horn, Jeffrey C. Grossman. Toward Designing Highly Conductive Polymer Electrolytes by Machine Learning Assisted Coarse-Grained Molecular Dynamics. Chemistry of Materials 2020, 32 (10) , 4144-4151. https://doi.org/10.1021/acs.chemmater.9b04830
  26. Austin D. Sendek, Gowoon Cheon, Mauro Pasta, Evan J. Reed. Quantifying the Search for Solid Li-Ion Electrolyte Materials by Anion: A Data-Driven Perspective. The Journal of Physical Chemistry C 2020, 124 (15) , 8067-8079. https://doi.org/10.1021/acs.jpcc.9b10650
  27. He Liu, Xin-Bing Cheng, Jia-Qi Huang, Hong Yuan, Yang Lu, Chong Yan, Gao-Long Zhu, Rui Xu, Chen-Zi Zhao, Li-Peng Hou, Chuanxin He, Stefan Kaskel, Qiang Zhang. Controlling Dendrite Growth in Solid-State Electrolytes. ACS Energy Letters 2020, 5 (3) , 833-843. https://doi.org/10.1021/acsenergylett.9b02660
  28. Andrew Z. Summers, Justin B. Gilmer, Christopher R. Iacovella, Peter T. Cummings, Clare McCabe. MoSDeF, a Python Framework Enabling Large-Scale Computational Screening of Soft Matter: Application to Chemistry-Property Relationships in Lubricating Monolayer Films. Journal of Chemical Theory and Computation 2020, 16 (3) , 1779-1793. https://doi.org/10.1021/acs.jctc.9b01183
  29. Myungjoon Kim, Byung Chul Yeo, Youngtae Park, Hyuck Mo Lee, Sang Soo Han, Donghun Kim. Artificial Intelligence to Accelerate the Discovery of N2 Electroreduction Catalysts. Chemistry of Materials 2020, 32 (2) , 709-720. https://doi.org/10.1021/acs.chemmater.9b03686
  30. Hong-Kang Tian, Zhe Liu, Yanzhou Ji, Long-Qing Chen, Yue Qi. Interfacial Electronic Properties Dictate Li Dendrite Growth in Solid Electrolytes. Chemistry of Materials 2019, 31 (18) , 7351-7359. https://doi.org/10.1021/acs.chemmater.9b01967
  31. Jonathan Lym, Geun Ho Gu, Yousung Jung, Dionisios G. Vlachos. Lattice Convolutional Neural Network Modeling of Adsorbate Coverage Effects. The Journal of Physical Chemistry C 2019, 123 (31) , 18951-18959. https://doi.org/10.1021/acs.jpcc.9b03370
  32. Nicolae C. Iovanac, Brett M. Savoie. Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment. The Journal of Physical Chemistry A 2019, 123 (19) , 4295-4302. https://doi.org/10.1021/acs.jpca.9b01398
  33. Dilip Krishnamurthy, Hasso Weiland, Amir Barati Farimani, Erin Antono, Josh Green, Venkatasubramanian Viswanathan. Machine Learning Based Approaches to Accelerate Energy Materials Discovery and Optimization. ACS Energy Letters 2019, 4 (1) , 187-191. https://doi.org/10.1021/acsenergylett.8b02278
  34. Xingyue Shi, Linming Zhou, Yuhui Huang, Chaohui Wu, Yongjun Wu, Juan Li, Zijian Hong. Machine learning assisted screening of binary alloys for metal-based anode materials. Journal of Energy Chemistry 2025, 104 , 62-68. https://doi.org/10.1016/j.jechem.2024.12.036
  35. Rituraj Dubey, Anees A. Ansari, Youngil Lee, Shili Gai, Ruichan Lv, Ziyue Ju, Shafiya Mohammad, Piaoping Yang, Laxman Singh. Molecular dynamics-to-machine learning for deep eutectics in energy storages. Renewable and Sustainable Energy Reviews 2025, 212 , 115358. https://doi.org/10.1016/j.rser.2025.115358
  36. Kexin Si, Zhipeng Sun, Huaxin Song, Xiangfen Jiang, Xuebin Wang. Machine learning-assisted design and prediction of materials for batteries based on alkali metals. Physical Chemistry Chemical Physics 2025, 27 (11) , 5423-5442. https://doi.org/10.1039/D4CP04214J
  37. Ye Min Thant, Taishiro Wakamiya, Methawee Nukunudompanich, Keisuke Kameda, Manabu Ihara, Sergei Manzhos. Kernel regression methods for prediction of materials properties: Recent developments. Chemical Physics Reviews 2025, 6 (1) https://doi.org/10.1063/5.0242118
  38. Bohayra Mortazavi. Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials. Advanced Energy Materials 2025, 15 (9) https://doi.org/10.1002/aenm.202403876
  39. Xueying Yuan, Xiupeng Chen, Yuanxin Zhou, Zhiao Yu, Xian Kong. Design principles of fluoroether solvents for lithium metal battery electrolytes unveiled by extensive molecular simulation and machine learning. Journal of Energy Chemistry 2025, 102 , 52-62. https://doi.org/10.1016/j.jechem.2024.10.021
  40. Rushuai Li, Wanyu Zhao, Ruimin Li, Chaolun Gan, Li Chen, Zhitao Wang, Xiaowei Yang. Leveraging machine learning for accelerated materials innovation in lithium-ion battery: a review. Journal of Energy Chemistry 2025, 59 https://doi.org/10.1016/j.jechem.2025.02.038
  41. Zhaojun Sun, Xin Li, Yiming Wu, Qilin Gu, Shiyou Zheng. Machine Learning‐Assisted Simulations and Predictions for Battery Interfaces. Advanced Intelligent Systems 2025, 27 https://doi.org/10.1002/aisy.202400626
  42. Qingqing Jia, Hongguang Liu, Xueping Wang, Qiantu Tao, Lifeng Zheng, Junjie Li, Wei Wang, Ziteng Liu, Xu Gu, Tianyu Shen, Shaoyi Hou, Zhong Jin, Jing Ma. Machine Learning‐Driven Mass Discovery and High‐Throughput Screening of Fluoroether‐Based Electrolytes for High‐Stability Lithium Metal Batteries. Angewandte Chemie International Edition 2025, https://doi.org/10.1002/anie.202424493
  43. Qingqing Jia, Hongguang Liu, Xueping Wang, Qiantu Tao, Lifeng Zheng, Junjie Li, Wei Wang, Ziteng Liu, Xu Gu, Tianyu Shen, Shaoyi Hou, Zhong Jin, Jing Ma. Machine Learning‐Driven Mass Discovery and High‐Throughput Screening of Fluoroether‐Based Electrolytes for High‐Stability Lithium Metal Batteries. Angewandte Chemie 2025, https://doi.org/10.1002/ange.202424493
  44. Haneen Alzamer, Russlan Jaafreh, Jung-Gu Kim, Kotiba Hamad. Artificial Intelligence and Li Ion Batteries: Basics and Breakthroughs in Electrolyte Materials Discovery. Crystals 2025, 15 (2) , 114. https://doi.org/10.3390/cryst15020114
  45. Yan Yuan, Bin Wang, Jin-Hao Zhang, Bin Zheng, Stanislav S. Fedotov, Hai Lu, Long Kong. Computational understanding and multiscale simulation of secondary batteries. Energy Storage Materials 2025, 75 , 104009. https://doi.org/10.1016/j.ensm.2025.104009
  46. Dawei Li, Chenhao Shen, Yuejiu Zheng, Jun Xu. Electrochemo‐Mechanical Degradation and Failure of Active Particles in High Energy Density Batteries: A Review. Small 2025, 21 (8) https://doi.org/10.1002/smll.202407740
  47. Jun Huang, Chen Li, Dongkai Jiang, Jingyi Gao, Lei Cheng, Guocheng Li, Hang Luo, Zheng‐Long Xu, Dong‐Myeong Shin, Yanming Wang, Yingying Lu, Yoonseob Kim. Solid‐State Electrolytes for Lithium Metal Batteries: State‐of‐the‐Art and Perspectives. Advanced Functional Materials 2025, 35 (1) https://doi.org/10.1002/adfm.202411171
  48. Dan Yang. Cell Components – Separators | Overview. 2025, 512-521. https://doi.org/10.1016/B978-0-323-96022-9.00238-3
  49. Zheng Wan, Zhenying Chen, Hao Chen, Yizhi Jiang, Jinhuan Zhang, Yidong Wang, Jindong Wang, Hao Sun, Zhongjie Zhu, Jinhui Zhu, Linyi Yang, Wei Ye, Shikun Zhang, Xing Xie, Yue Zhang, Xiaodong Zhuang, Xiao He, Jinrong Yang. XRDMatch: a semi-supervised learning framework to efficiently discover room temperature lithium superionic conductors. Energy & Environmental Science 2024, 17 (24) , 9487-9498. https://doi.org/10.1039/D4EE02970D
  50. Jingyuan Zhao, Zhilong Lv, Di Li, Xuning Feng, Zhenghong Wang, Yuyan Wu, Dapai Shi, Michael Fowler, Andrew F. Burke. Battery engineering safety technologies (BEST): M5 framework of mechanisms, modes, metrics, modeling, and mitigation. eTransportation 2024, 22 , 100364. https://doi.org/10.1016/j.etran.2024.100364
  51. Chenfeng Ding, Penghui Ji, Tongtong Li, Ting Guo, Zhong Xu, Taehoon Kim, Hui Zhang, Jiayu Wan, Luis K. Ono, Yabing Qi. Photoemission spectroscopy of battery materials. Applied Physics Reviews 2024, 11 (4) https://doi.org/10.1063/5.0235835
  52. Pengcheng Xue, Rui Qiu, Chuchuan Peng, Zehang Peng, Kui Ding, Rui Long, Liang Ma, Qifeng Zheng. Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook. Advanced Science 2024, 11 (48) https://doi.org/10.1002/advs.202410065
  53. Xinyuan Shan, Zhaowei Song, Hang Ding, Lengwan Li, Yuhang Tian, Alexei P. Sokolov, Ming Tian, Kang Xu, Peng-Fei Cao. Polymer electrolytes with high cation transport number for rechargeable Li–metal batteries: current status and future direction. Energy & Environmental Science 2024, 17 (22) , 8457-8481. https://doi.org/10.1039/D4EE03097D
  54. Jiayi Du, Jun Guo, Wei Liu, Ziwei Li, Gang Huang, Xinbo Zhang. A universal strategy of multi-objective active learning to accelerate the discovery of organic electrode molecules. Science China Chemistry 2024, 67 (11) , 3681-3687. https://doi.org/10.1007/s11426-024-2163-1
  55. Yige Xiong, Die Zhang, Xiaorong Ruan, Shanbao Jiang, Xueqin Zou, Wei Yuan, Xiuxue Liu, Yapeng Zhang, Zeqi Nie, Donghai Wei, Yubin Zeng, Peng Cao, Guanhua Zhang. Artificial intelligence in rechargeable battery: Advancements and prospects. Energy Storage Materials 2024, 73 , 103860. https://doi.org/10.1016/j.ensm.2024.103860
  56. Amirreza Sazvar, Masoumeh Hajibandeh, Pariya Vafaei, Elham Hosseinzadeh, Masoud Jabbari. Review of recent progress in sintering of solid-state batteries: Application and modelling. Journal of Energy Storage 2024, 101 , 113863. https://doi.org/10.1016/j.est.2024.113863
  57. Mingchao Wang. Charging dependent electro-chemo-mechanical coupling behavior of polymer electrolytes containing immobilized anions. Mechanics of Materials 2024, 198 , 105143. https://doi.org/10.1016/j.mechmat.2024.105143
  58. Yang Luo, Zhonghao Rao, Xiaofei Yang, Changhong Wang, Xueliang Sun, Xianfeng Li. Safety concerns in solid-state lithium batteries: from materials to devices. Energy & Environmental Science 2024, 17 (20) , 7543-7565. https://doi.org/10.1039/D4EE02358G
  59. Yu Ding, Hui Shang, Changze Yang, Liang Zhao, Aijun Duan. Identifying efficient and inexpensive hydrodesulfurization catalysts through Machine Learning-Assisted analysis of Metal–Sulfur bonds in transition metal sulfides. Chemical Engineering Science 2024, 298 , 120337. https://doi.org/10.1016/j.ces.2024.120337
  60. Xiaoru Wang, Rui Yan, Huizhe Niu, Zemin He, Wanli He, Zongcheng Miao. Applications of liquid crystal in lithium battery electrolytes. Journal of Energy Storage 2024, 100 , 113687. https://doi.org/10.1016/j.est.2024.113687
  61. Qianyu Hu, Kunfeng Chen, Jinyu Li, Tingting Zhao, Feng Liang, Dongfeng Xue. Speeding up the development of solid state electrolyte by machine learning. Next Energy 2024, 5 , 100159. https://doi.org/10.1016/j.nxener.2024.100159
  62. Seongmin Kim, Jiaxin Xu, Wenjie Shang, Zhihao Xu, Eungkyu Lee, Tengfei Luo. A review on machine learning-guided design of energy materials. Progress in Energy 2024, 6 (4) , 042005. https://doi.org/10.1088/2516-1083/ad7220
  63. S. Esmizadeh, L. Cabras, M. Serpelloni, T. Dev, V. Oancea, E. Knobbe, M. Lachner, A. Salvadori. A review on modeling of nucleation and growth of Li dendrites in solid electrolytes. Journal of Energy Storage 2024, 97 , 112897. https://doi.org/10.1016/j.est.2024.112897
  64. Qiangxiang Zhai, Hongmin Jiang, Nengbing Long, Qiaoling Kang, Xianhe Meng, Mingjiong Zhou, Lijing Yan, Tingli Ma. Machine learning for full lifecycle management of lithium-ion batteries. Renewable and Sustainable Energy Reviews 2024, 202 , 114647. https://doi.org/10.1016/j.rser.2024.114647
  65. Alireza Valizadeh, Mohammad Hossein Amirhosseini. Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends. SN Computer Science 2024, 5 (6) https://doi.org/10.1007/s42979-024-03046-2
  66. Yoonsu Shim, Incheol Jeong, Junpyo Hur, Hyoungjeen Jeen, Seung‐Taek Myung, Kang Taek Lee, Seungbum Hong, Jong Min Yuk, Chan‐Woo Lee. Data‐Driven Design of NASICON‐Type Electrodes Using Graph‐Based Neural Networks. Batteries & Supercaps 2024, 8 https://doi.org/10.1002/batt.202400186
  67. Guochang Huang, Fuqiang Huang, Wujie Dong. Machine learning in energy storage material discovery and performance prediction. Chemical Engineering Journal 2024, 492 , 152294. https://doi.org/10.1016/j.cej.2024.152294
  68. Jieyan Li, Xin Chen, Saz Muhammad, Shubham Roy, Haiyan Huang, Chen Yu, Zia Ullah, Zeru Wang, Yinghe Zhang, Ke Wang, Bing Guo. Development of solid polymer electrolytes for solid-state lithium battery applications. Materials Today Energy 2024, 43 , 101574. https://doi.org/10.1016/j.mtener.2024.101574
  69. Xingyue Shi, Linming Zhou, Yuhui Huang, Yongjun Wu, Zijian Hong. A review on the applications of graph neural networks in materials science at the atomic scale. Materials Genome Engineering Advances 2024, 2 (2) https://doi.org/10.1002/mgea.50
  70. Xinxin Liu, Kexin Fan, Xinmeng Huang, Jiankai Ge, Yujie Liu, Haisu Kang. Recent advances in artificial intelligence boosting materials design for electrochemical energy storage. Chemical Engineering Journal 2024, 490 , 151625. https://doi.org/10.1016/j.cej.2024.151625
  71. Peichen Zhong, Bowen Deng, Tanjin He, Zhengyan Lun, Gerbrand Ceder. Deep learning of experimental electrochemistry for battery cathodes across diverse compositions. Joule 2024, 8 (6) , 1837-1854. https://doi.org/10.1016/j.joule.2024.03.010
  72. Yanbin Ning, Feng Yang, Yan Zhang, Zhuomin Qiang, Geping Yin, Jiajun Wang, Shuaifeng Lou. Bridging multimodal data and battery science with machine learning. Matter 2024, 7 (6) , 2011-2032. https://doi.org/10.1016/j.matt.2024.04.030
  73. Xiaozhao Liu, Volodymyr Koverga, Hoai T. Nguyen, Anh T. Ngo, Tao Li. Exploring solvation structure and transport behavior for rational design of advanced electrolytes for next generation of lithium batteries. Applied Physics Reviews 2024, 11 (2) https://doi.org/10.1063/5.0187154
  74. Chen Liao, Bingning Wang, Hieu Doan, Seoung-Bum Son, Daniel Abraham, Stephen Trask, Andrew Jansen, Kang Xu. Data-driven Design of Electrolyte Additives for High-Performance 5 V LiNi0.5Mn1.5O4 Cathodes. 2024https://doi.org/10.21203/rs.3.rs-4420883/v1
  75. Mingjian Wen, Matthew K. Horton, Jason M. Munro, Patrick Huck, Kristin A. Persson. An equivariant graph neural network for the elasticity tensors of all seven crystal systems. Digital Discovery 2024, 3 (5) , 869-882. https://doi.org/10.1039/D3DD00233K
  76. Jin Li, Meisa Zhou, Hong‐Hui Wu, Lifei Wang, Jian Zhang, Naiteng Wu, Kunming Pan, Guilong Liu, Yinggan Zhang, Jiajia Han, Xianming Liu, Xiang Chen, Jiayu Wan, Qiaobao Zhang. Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte. Advanced Energy Materials 2024, 14 (20) https://doi.org/10.1002/aenm.202304480
  77. Kaiyi Yang, Lisheng Zhang, Wentao Wang, Chengwu Long, Shichun Yang, Tao Zhu, Xinhua Liu. Multiscale modeling for enhanced battery health analysis: Pathways to longevity. Carbon Neutralization 2024, 3 (3) , 348-385. https://doi.org/10.1002/cnl2.124
  78. Jingyuan Zhao, Xuning Feng, Quanquan Pang, Michael Fowler, Yubo Lian, Minggao Ouyang, Andrew F. Burke. Battery safety: Machine learning-based prognostics. Progress in Energy and Combustion Science 2024, 102 , 101142. https://doi.org/10.1016/j.pecs.2023.101142
  79. Yuyao Zhang, Tingjie Zhan, Yang Sun, Lun Lu, Baoliang Chen. Revolutionizing Solid‐State NASICON Sodium Batteries: Enhanced Ionic Conductivity Estimation through Multivariate Experimental Parameters Leveraging Machine Learning. ChemSusChem 2024, 17 (6) https://doi.org/10.1002/cssc.202301284
  80. Prit Thakkar, Sachi Khatri, Drashti Dobariya, Darpan Patel, Bishwajit Dey, Alok Kumar Singh. Advances in materials and machine learning techniques for energy storage devices: A comprehensive review. Journal of Energy Storage 2024, 81 , 110452. https://doi.org/10.1016/j.est.2024.110452
  81. Yinghe Wang, Shu Li, Shuai Li, Minghua Chen. Prediction, interpretation and extrapolation for shear modulus and bulk modulus of solid-state electrolytes based on machine learning. Materials Today Communications 2024, 38 , 108294. https://doi.org/10.1016/j.mtcomm.2024.108294
  82. Sue Sin Chong, Yi Sheng Ng, Hui-Qiong Wang, Jin-Cheng Zheng. Advances of machine learning in materials science: Ideas and techniques. Frontiers of Physics 2024, 19 (1) https://doi.org/10.1007/s11467-023-1325-z
  83. Sagnik Acharya, P. Viswesh, M.K. Sridhar, Anil D. Pathak, Henu Sharma, Aqsa Nazir, Arvind Kasbe, Kisor K. Sahu. Artificial intelligence and machine learning in battery materials and their applications. 2024, 639-676. https://doi.org/10.1016/B978-0-323-91304-1.00012-5
  84. Prasshanth C.V., Arun Kumar Lakshminarayanan, Brindha Ramasubramanian, Seeram Ramakrishna. Progress of machine learning in materials design for Li-Ion battery. Next Materials 2024, 2 , 100145. https://doi.org/10.1016/j.nxmate.2024.100145
  85. Tian-Xing Yang, Peng Dou. Prediction of Bulk and Shear Moduli of Oxides in Ods Steels by Machine Learning and First-Principles Calculations. 2024https://doi.org/10.2139/ssrn.4863874
  86. Xinhua Liu, Kaiyi Yang, Lisheng Zhang, Wentao Wang, Sida Zhou, Billy Wu, Mengyu Xiong, Shichun Yang, Rui Tan. A Fast Forward Prediction Framework for Energy Materials Design Based on Machine Learning Methods. Energy Material Advances 2024, 5 https://doi.org/10.34133/energymatadv.0131
  87. Varad Mahajani, Nikhil Koratkar. Toward Practical Alloy Anode Based Solid State Batteries. Small 2023, 45 https://doi.org/10.1002/smll.202306388
  88. Shu Zhang, Jun Ma, Shanmu Dong, Guanglei Cui. Designing All-Solid-State Batteries by Theoretical Computation: A Review. Electrochemical Energy Reviews 2023, 6 (1) https://doi.org/10.1007/s41918-022-00143-9
  89. Li Zheng, Shuqing Zhang, Hao Huang, Ruxiang Liu, Mian Cai, Yinghui Bian, Long Chang, Huiping Du. Artificial intelligence-driven rechargeable batteries in multiple fields of development and application towards energy storage. Journal of Energy Storage 2023, 73 , 108926. https://doi.org/10.1016/j.est.2023.108926
  90. Aysegul Kilic, Burcu Oral, Damla Eroglu, Ramazan Yildirim. Machine learning for beyond Li-ion batteries: Powering the research. Journal of Energy Storage 2023, 73 , 109057. https://doi.org/10.1016/j.est.2023.109057
  91. Xiaofei Yang, Qianwen Yin, Changhong Wang, Kieran Doyle-Davis, Xueliang Sun, Xianfeng Li. Towards practically accessible high-voltage solid-state lithium batteries: From fundamental understanding to engineering design. Progress in Materials Science 2023, 140 , 101193. https://doi.org/10.1016/j.pmatsci.2023.101193
  92. Qi ZHANG, Chao PENG, DongFeng XUE. Data-driven approaches enabling the screening and design of promising materials for energy storage batteries. SCIENTIA SINICA Technologica 2023, 140 https://doi.org/10.1360/SST-2023-0157
  93. Yawei Chen, Yue Liu, Zixu He, Liang Xu, Peiping Yu, Qintao Sun, Wanxia Li, Yulin Jie, Ruiguo Cao, Tao Cheng, Shuhong Jiao. Artificial intelligence for the understanding of electrolyte chemistry and electrode interface in lithium battery. National Science Open 2023, 91 , 20230039. https://doi.org/10.1360/nso/20230039
  94. Sheng Gong, Keqiang Yan, Tian Xie, Yang Shao-Horn, Rafael Gomez-Bombarelli, Shuiwang Ji, Jeffrey C. Grossman. Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity. Science Advances 2023, 9 (45) https://doi.org/10.1126/sciadv.adi3245
  95. Cengliang Shan, Yanli Wang, Minhui Liang, Kuan Lu, Chen Xiong, Wei Hu, Baijun Liu. A comprehensive review of single ion-conducting polymer electrolytes as a key component of lithium metal batteries: From structural design to applications. Energy Storage Materials 2023, 63 , 102955. https://doi.org/10.1016/j.ensm.2023.102955
  96. Yang Luo, Xiaofei Yang, Changhong Wang, Adam Fraser, Hongzhang Zhang, Xueliang Sun, Xianfeng Li. Advanced metal anodes and their interface design toward safe metal batteries: A comprehensive review. Progress in Materials Science 2023, 139 , 101171. https://doi.org/10.1016/j.pmatsci.2023.101171
  97. Yong Qiu, Xu Zhang, Yun Tian, Zhen Zhou. Machine learning promotes the development of all-solid-state batteries. Chinese Journal of Structural Chemistry 2023, 42 (9) , 100118. https://doi.org/10.1016/j.cjsc.2023.100118
  98. Tong Jin, Jun Song Chen, Xiao Chun Chen, Nian Wu Li, Le Yu. Artificial interphase layers for Li metal anode, what’s next?. Next Energy 2023, 1 (3) , 100040. https://doi.org/10.1016/j.nxener.2023.100040
  99. Marco Ragone, Reza Shahabazian-Yassar, Farzad Mashayek, Vitaliy Yurkiv. Deep learning modeling in microscopy imaging: A review of materials science applications. Progress in Materials Science 2023, 138 , 101165. https://doi.org/10.1016/j.pmatsci.2023.101165
  100. Trang Thi Vu, Hyeong Jun Cheon, Seo Young Shin, Ganghoon Jeong, Eunsol Wi, Mincheol Chang. Hybrid electrolytes for solid-state lithium batteries: Challenges, progress, and prospects. Energy Storage Materials 2023, 61 , 102876. https://doi.org/10.1016/j.ensm.2023.102876
Load more citations
Download PDF
Go to ACS Central Science
Get e-Alerts
Get e-Alerts

ACS Central Science

Cite this: ACS Cent. Sci. 2018, 4, 8, 996–1006
Click to copy citationCitation copied!
https://doi.org/10.1021/acscentsci.8b00229
Published August 10, 2018

Copyright © 2018 American Chemical Society. This publication is licensed under these Terms of Use.

Request reuse permissions

Article Views

17k

Altmetric

-

Citations

207
Learn about these metrics

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.

Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.

The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.

  • Abstract

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 1

    Figure 1. Parity plots comparing the elastic properties: (a) shear modulus G, and elastic constants (b) C11, (c) C12, and (d) C44 predicted by the machine-learning models to the DFT-calculated values. The shear modulus is predicted using CGCNN, and the elastic constants C11 and C44 are predicted using gradient boosting regression while C12 is predicted using kernel ridge regression. The parity plot for shear modulus is on 680 test data points while that for the elastic constants contains all available data (170 points) where each prediction is a cross-validated value.

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 2

    Figure 2. Contribution of hydrostatic stress, deviatoric stress, and surface tension to the stability parameter as a function of surface roughness wavenumber. The surface tension term starts dominating at high k and ultimately stabilizes the interface after k = kcrit. The contributions are plotted for a material with shear modulus ratio G/GLi = 1 and Poisson’s ratio ν = 0.33 which is not stable (χ > 0) at k = 108 m–1. The red line shows the fraction of surface tension contribution to the stability parameter obtained by dividing the absolute value of its contribution by the sum of absolute values of all components.

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 3

    Figure 3. Results of isotropic screening for 12 950 Li-containing compounds. Distribution of ensemble averaged (a) stability parameter for isotropic Li–solid electrolyte interfaces at k = 108 m–1 and (b) critical wavelength of surface roughness required for stability. None of the materials in the database can be stabilized without the aid of surface tension. The required critical surface roughness wavenumber depends on the contribution of the stress term in the stability parameter.

    High Resolution Image
    Download MS PowerPoint Slide

    Figure 4

    Figure 4. Isotropic stability diagram showing the position of all solid electrolytes involved in the screening. GLi is the shear modulus of Li = 3.4 GPa. The critical G/GLi line separating the stable and unstable regions depends weakly on the Poisson’s ratio, so the lines corresponding to νs = 0.33 and 0.5 are good indicators for assessment of stability. The darker regions indicate more number of materials in the region.

    High Resolution Image
    Download MS PowerPoint Slide

  • References

    This article references 97 other publications.

    1. 1
      Sripad, S.; Viswanathan, V. Evaluation of Current, Future, and Beyond Li-Ion Batteries for the Electrification of Light Commercial Vehicles: Challenges and Opportunities. J. Electrochem. Soc. 2017, 164, E3635– E3646,  DOI: 10.1149/2.0671711jes
      1
      Evaluation of Current, Future, and Beyond Li-Ion Batteries for the Electrification of Light Commercial Vehicles: Challenges and Opportunities
      Sripad, Shashank; Viswanathan, Venkatasubramanian
      Journal of the Electrochemical Society (2017), 164 (11), E3635-E3646CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
      While the electrification of the transportation sector is underway with a consistently increasing market share, segments like light com. vehicles (LCVs) have not seen any significant market penetration. The major barriers are primarily in the shortcomings in specific energy, power, and cost assocd. with Li-ion batteries. With an initiative from several automakers to electrify other segments, there is a need to critically examine the performance requirements and to understand the factors governing such a transition in the near-term and long-term. In this study, a systematic methodol. framework is developed to analyze the performance demands for electrification through an approach that couples considerations for battery chemistries, load profiles based on a set of vehicle-specific drive cycles, and finally applying these loads to a battery pack that solves a 1-D thermally coupled battery model within the AutoLion-ST framework. Using this framework, the performance is analyzed of a fully elec. LCV over its lifetime under various driving conditions and explore the trade-offs between battery metrics and vehicle design parameters. It was found that in order to enable a driving range of over 400-mi for LCVs at a realistic battery pack wt., specific energies of over 400 Wh/kg at the cell-level and 200 Wh/kg at the pack-level needs to be achieved. A crucial factor that could bring down both the energy requirements and cost is through a vehicle re-design that lowers the drag coeff. to about 0.3.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhsFClt77J&md5=f03859a781a14cdf43ac4f07bc79fd7d
    2. 2
      Sripad, S.; Viswanathan, V. Performance Metrics Required of Next-Generation Batteries to Make a Practical Electric Semi Truck. ACS Energy Lett. 2017, 2, 1669– 1673,  DOI: 10.1021/acsenergylett.7b00432
      2
      Performance Metrics Required of Next-Generation Batteries to Make a Practical Electric Semi Truck
      Sripad, Shashank; Viswanathan, Venkatasubramanian
      ACS Energy Letters (2017), 2 (7), 1669-1673CODEN: AELCCP; ISSN:2380-8195. (American Chemical Society)
      There is enormous interest around elec. Semi Truck and in order to understand the practical limitations imposed by the battery pack, a rigorous and thorough anal. considering battery metrics as well as vehicle design parameters is required. In this study, we find that an elec. semi truck, such as the envisioned Tesla Semi, would be limited to a driving range of 300 mi, a small payload capacity, and a prohibitively high cost. Enabling a longer driving range of 600 mi needed by the current stds. of the trucking industry might require a transition to beyond Li-ion batteries.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVGrs7fL&md5=9ec6ad16dc50a0fd2d41a74a950d9fa6
    3. 3
      Moore, M. D. Misconceptions of Electric Aircraft and their Emerging Aviation Markets; AIAA SciTech Forum: American Institute of Aeronautics and Astronautics, 2014.
      There is no corresponding record for this reference.
    4. 4
      Cheng, X.-B.; Zhang, R.; Zhao, C.-Z.; Zhang, Q. Toward Safe Lithium Metal Anode in Rechargeable Batteries: A Review. Chem. Rev. 2017, 117, 10403– 10473,  DOI: 10.1021/acs.chemrev.7b00115
      4
      Toward Safe Lithium Metal Anode in Rechargeable Batteries: A Review
      Cheng, Xin-Bing; Zhang, Rui; Zhao, Chen-Zi; Zhang, Qiang
      Chemical Reviews (Washington, DC, United States) (2017), 117 (15), 10403-10473CODEN: CHREAY; ISSN:0009-2665. (American Chemical Society)
      A review is presented. The lithium metal battery is strongly considered to be one of the most promising candidates for high-energy-d. energy storage devices in our modern and technol.-based society. However, uncontrollable lithium dendrite growth induces poor cycling efficiency and severe safety concerns, dragging lithium metal batteries out of practical applications. This review presents a comprehensive overview of the lithium metal anode and its dendritic lithium growth. First, the working principles and tech. challenges of a lithium metal anode are underscored. Specific attention is paid to the mechanistic understandings and quant. models for solid electrolyte interphase (SEI) formation, lithium dendrite nucleation, and growth. On the basis of previous theor. understanding and anal., recently proposed strategies to suppress dendrite growth of lithium metal anode and some other metal anodes are reviewed. A section dedicated to the potential of full-cell lithium metal batteries for practical applications is included. A general conclusion and a perspective on the current limitations and recommended future research directions of lithium metal batteries are presented. The review concludes with an attempt at summarizing the theor. and exptl. achievements in lithium metal anodes and endeavors to realize the practical applications of lithium metal batteries.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXht1eku7bK&md5=f83e2bc869af2a2d65226611e96c8227
    5. 5
      Lin, D.; Liu, Y.; Cui, Y. Reviving the lithium metal anode for high-energy batteries. Nat. Nanotechnol. 2017, 12, 194– 206,  DOI: 10.1038/nnano.2017.16
      5
      Reviving the lithium metal anode for high-energy batteries
      Lin, Dingchang; Liu, Yayuan; Cui, Yi
      Nature Nanotechnology (2017), 12 (3), 194-206CODEN: NNAABX; ISSN:1748-3387. (Nature Publishing Group)
      A review is given. Li-ion batteries have had a profound impact on our daily life, but inherent limitations make it difficult for Li-ion chemistries to meet the growing demands for portable electronics, elec. vehicles and grid-scale energy storage. Therefore, chemistries beyond Li-ion are currently being investigated and need to be made viable for com. applications. The use of metallic Li is one of the most favored choices for next-generation Li batteries, esp. Li-S and Li-air systems. After falling into oblivion for several decades because of safety concerns, metallic Li is now ready for a revival, thanks to the development of investigative tools and nanotechnol.-based solns. Here, we 1st summarize the current understanding on Li anodes, then highlight the recent key progress in materials design and advanced characterization techniques, and finally discuss the opportunities and possible directions for future development of Li anodes in applications.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXmtVyitr4%253D&md5=07d29fc449ccc1f6c941b4c7692a8639
    6. 6
      Christensen, J.; Albertus, P.; Sanchez-Carrera, R. S.; Lohmann, T.; Kozinsky, B.; Liedtke, R.; Ahmed, J.; Kojic, A. A Critical Review of Li/Air Batteries. J. Electrochem. Soc. 2011, 159, R1– R30,  DOI: 10.1149/2.086202jes
      There is no corresponding record for this reference.
    7. 7
      Xu, W.; Wang, J.; Ding, F.; Chen, X.; Nasybulin, E.; Zhang, Y.; Zhang, J.-G. Lithium metal anodes for rechargeable batteries. Energy Environ. Sci. 2014, 7, 513– 537,  DOI: 10.1039/C3EE40795K
      7
      Lithium metal anodes for rechargeable batteries
      Xu, Wu; Wang, Jiulin; Ding, Fei; Chen, Xilin; Nasybulin, Eduard; Zhang, Yaohui; Zhang, Ji-Guang
      Energy & Environmental Science (2014), 7 (2), 513-537CODEN: EESNBY; ISSN:1754-5706. (Royal Society of Chemistry)
      A review. Lithium (Li) metal is an ideal anode material for rechargeable batteries due to its extremely high theor. specific capacity (3860 mA h g-1), low d. (0.59 g cm-3) and the lowest neg. electrochem. potential (-3.040 V vs. the std. hydrogen electrode). Unfortunately, uncontrollable dendritic Li growth and limited Coulombic efficiency during Li deposition/stripping inherent in these batteries have prevented their practical applications over the past 40 years. With the emergence of post-Li-ion batteries, safe and efficient operation of Li metal anodes has become an enabling technol. which may det. the fate of several promising candidates for the next generation energy storage systems, including rechargeable Li-air batteries, Li-S batteries, and Li metal batteries which utilize intercalation compds. as cathodes. In this paper, various factors that affect the morphol. and Coulombic efficiency of Li metal anodes have been analyzed. Technologies utilized to characterize the morphol. of Li deposition and the results obtained by modeling of Li dendrite growth have also been reviewed. Finally, recent development and urgent need in this field are discussed.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsFaktL8%253D&md5=3813f919eb7ca4218cfb562f54c9382a
    8. 8
      Tikekar, M. D.; Choudhury, S.; Tu, Z.; Archer, L. A. Design principles for electrolytes and interfaces for stable lithium-metal batteries. Nat. Energy 2016, 1, 16114,  DOI: 10.1038/nenergy.2016.114
      8
      Design principles for electrolytes and interfaces for stable lithium-metal batteries
      Tikekar, Mukul D.; Choudhury, Snehashis; Tu, Zhengyuan; Archer, Lynden A.
      Nature Energy (2016), 1 (9), 16114CODEN: NEANFD; ISSN:2058-7546. (Nature Publishing Group)
      A review. The future of electrochem. energy storage hinges on the advancement of science and technol. that enables rechargeable batteries that utilize reactive metals as anodes. With specific capacity more than ten times that of the LiC6 anode used in present-day lithium-ion batteries, cells based on Li-metal anodes are of particular interest. Effective strategies for stabilizing the anode in such cells are now understood to be a requirement for progress on exceptional storage technologies, including Li-S and Li-O2 batteries. Multiple challenges-parasitic reactions of Li-metal with liq. electrolytes, unstable and dendritic electrodeposition, and dendrite-induced short circuits-derailed early efforts to commercialize such lithium-metal batteries. Here we consider approaches for rationally designing electrolytes and Li-metal/electrolyte interfaces for stable, dendrite-free operation of lithium-metal batteries. On the basis of fundamental understanding of the failure modes of reactive metal anodes, we discuss the key variables that govern the stability of electrodeposition at the Li anode and propose a universal framework for designing stable electrolytes and interfaces for lithium-metal batteries.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVersL8%253D&md5=3627028eaacfbf47fd2a8691cfddcef1
    9. 9
      Aurbach, D.; Zinigrad, E.; Teller, H.; Dan, P. Factors Which Limit the Cycle Life of Rechargeable Lithium (Metal) Batteries. J. Electrochem. Soc. 2000, 147, 1274– 1279,  DOI: 10.1149/1.1393349
      9
      Factors which limit the cycle life of rechargeable lithium (metal) batteries
      Aurbach, D.; Zinigrad, E.; Teller, H.; Dan, P.
      Journal of the Electrochemical Society (2000), 147 (4), 1274-1279CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
      Failure mechanisms due to high charging rates of rechargeable lithium batteries comprised of Li anodes, Li0.3MnO2 cathodes (tunneled structure), and electrolyte solns. based on the combination of 1,3-dioxolane, LiAsF6, and tributylamine (antipolymn. stabilizer) were explored with the aid of postmortem anal. It was found that at high charging rates, lithium deposition produces small grains, which are too reactive toward the electrolyte soln., in spite of the excellent passivation of lithium in this soln. In practical batteries such as AA cells with spirally wound configurations, the amt. of soln. is relatively small, and the soln. is spread throughout the battery in a thin layer. Therefore, upon cycling, the Li-soln. reactions deplete the amt. of the soln. below a crit. value, so that only part of the active materials continues to function. This leads to a pronounced increase in the internal resistance of these batteries, which fail as a result of their high impedance and the decrease in the effective working electrodes area. Another failure mechanism relates to the extremely high charge-discharge current densities developed as the active electrode area decreases. These high currents, developed after prolonged cycling, lead to the formation of dendrites that short-circuit the battery, thus terminating its life.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXisVygtLo%253D&md5=9c7dc9d384db266edbddf0a3ae3d6f0c
    10. 10
      Aurbach, D.; Zinigrad, E.; Cohen, Y.; Teller, H. A short review of failure mechanisms of lithium metal and lithiated graphite anodes in liquid electrolyte solutions. Solid State Ionics 2002, 148, 405– 416,  DOI: 10.1016/S0167-2738(02)00080-2
      10
      A short review of failure mechanisms of lithium metal and lithiated graphite anodes in liquid electrolyte solutions
      Aurbach, Doron; Zinigrad, Ella; Cohen, Yaron; Teller, Hanan
      Solid State Ionics (2002), 148 (3,4), 405-416CODEN: SSIOD3; ISSN:0167-2738. (Elsevier Science B.V.)
      A review. Li electrodes in any relevant electrolyte soln. (i.e., polar aprotic) are covered by surface films of a very complicated structure. It was found that even in cases where the surface films formed on lithium contain elastomers, or where the lithium metal reactivity is reduced by doping with elements such as N, As, Al, Mg, Ca, etc., it is impossible to achieve sufficient passivation with lithium electrodes and liq. solns. Passivation is considerably worsened when Li electrodes are operated at high rates (esp. at high charging, Li deposition rates). Thus, there is no way that rechargeable Li batteries can compete with Li-ion batteries in any application that requires high charging rates (e.g., in powering portable electronic devices). The electrochem. behavior of lithiated graphite electrodes also depends on passivation phenomena. The surface films formed on lithiated graphite are similar to those formed on Li metal in the same solns. The vol. changes of graphite electrodes during Li insertion-deinsertion are small enough to enable their reasonable passivation in a variety of electrolyte solns. A crit. factor that dets. the stability of graphite electrodes is their morphol. It was found that the shape of graphite particles plays a key role in their application as active mass in anodes for Li-ion batteries.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD38XktVWgurY%253D&md5=24417ffbcd25ad6ffaba30ab7d00e3d4
    11. 11
      Steiger, J.; Kramer, D.; Mönig, R. Mechanisms of dendritic growth investigated by in situ light microscopy during electrodeposition and dissolution of lithium. J. Power Sources 2014, 261, 112– 119,  DOI: 10.1016/j.jpowsour.2014.03.029
      11
      Mechanisms of dendritic growth investigated by in situ light microscopy during electrodeposition and dissolution of lithium
      Steiger, Jens; Kramer, Dominik; Moenig, Reiner
      Journal of Power Sources (2014), 261 (), 112-119CODEN: JPSODZ; ISSN:0378-7753. (Elsevier B.V.)
      Batteries with metallic lithium anodes offer improved volumetric and gravimetric energy densities; therefore, future batteries including the promising lithium-sulfur and lithium-air systems would benefit from them. The electrodeposition of lithium metal - which is an unwanted incident in lithium ion systems - often results in fine filaments or moss, called dendritic lithium, which leads to strong capacity fading and the danger of internal short circuiting. To study the mechanisms of dendritic growth and the behavior during lithium dissoln., lithium deposits have been obsd. in situ in 1 M LiPF6 in EC:DMC by light microscopy. The high resoln. optical microscopy provided information on the growth and electrodissoln. of single lithium filaments. The growth areas could be identified in detail: The lithium wires can grow either from the substrate-lithium interface, at kinks or in a region at or close to the tip. Based on these observations, we suggest a growth model for lithium filaments predicated on defect-based insertion of lithium at the aforementioned locations. This type of growth is not compatible with previous models of dendritic growth, for example, it is hardly influenced by elec. fields at the tip and does not depend on the direction of the elec. field.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXnsl2murc%253D&md5=2db8d2782ea70c21bf11b86d6c3618fd
    12. 12
      Albertus, P.; Babinec, S.; Litzelman, S.; Newman, A. Status and challenges in enabling the lithium metal electrode for high-energy and low-cost rechargeable batteries. Nat. Energy 2018, 3, 16– 21,  DOI: 10.1038/s41560-017-0047-2
      12
      Status and challenges in enabling the lithium metal electrode for high-energy and low-cost rechargeable batteries
      Albertus, Paul; Babinec, Susan; Litzelman, Scott; Newman, Aron
      Nature Energy (2018), 3 (1), 16-21CODEN: NEANFD; ISSN:2058-7546. (Nature Research)
      Enabling the reversible lithium metal electrode is essential for surpassing the energy content of today's lithium-ion cells. Although lithium metal cells for niche applications have been developed already, efforts are underway to create rechargeable lithium metal batteries that can significantly advance vehicle electrification and grid energy storage. In this Perspective, we focus on three tasks to guide and further advance the reversible lithium metal electrode. First, we summarize the state of research and com. efforts in terms of four key performance parameters, and identify addnl. performance parameters of interest. We then advocate for the use of limited lithium (≤30 μm) to ensure early identification of tech. challenges assocd. with stable and dendrite-free cycling and a more rapid transition to com. relevant designs. Finally, we provide a cost target and outline material costs and manufg. methods that could allow lithium metal cells to reach 100 US$ kWh-1.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXitVehurY%253D&md5=569e6865fb3a225323353f9e9f39fddb
    13. 13
      Aurbach, D.; Markovsky, B.; Shechter, A.; Ein-Eli, Y.; Cohen, H. A Comparative Study of Synthetic Graphite and Li Electrodes in Electrolyte Solutions Based on Ethylene Carbonate-Dimethyl Carbonate Mixtures. J. Electrochem. Soc. 1996, 143, 3809– 3820,  DOI: 10.1149/1.1837300
      13
      A comparative study of synthetic graphite and Li electrodes in electrolyte solutions based on ethylene carbonate-dimethyl carbonate mixtures
      Aurbach, D.; Markovsky, B.; Shechter, A.; Ein-Eli, Y.; Cohen, H.
      Journal of the Electrochemical Society (1996), 143 (12), 3809-3820CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
      This work entails a comparative study of both Li and synthetic graphite electrodes in electrolyte solns. based on ethylene and di-Me carbonates (EC-DMC) and the impact of the salt used [LiAsF6, LiClO4, LiPF6, LiBF4, and LiN(SO2CF3)2]. The presence of some additives in solns. (e.g., Li2CO3, CO2, tributylamine) and the effect of the particle size of the carbon on the behavior of the electrode were studied. The correlation between the surface chem., the morphol., and the performance of Li and graphite electrodes was explored using surface sensitive FTIR and x-ray and photoelectron spectroscopies, impedance spectroscopy, x-ray diffraction and SEM in conjunction with std. electrochem. techniques. Synthetic graphite anodes could be cycled (Li intercalation-deintercalation) hundreds of times at a capacity close to the optimal (x → 1 in LixC6) in EC-DMC solns. due to the formation of highly stable and passivating surface films in which EC redn. products such as (CH2OCO2Li)2 are the major constituents. The cycling efficiency of Li metal anodes in these solns., however, is lower than that obtained in ethereal solns. and seems to be too low for Li-metal liq. electrolyte, rechargeable battery application. The connection between the soln. compn. and the electrode's performance is discussed.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2sXhsFehsg%253D%253D&md5=02e52a156683cedd7ca6901f81eb7313
    14. 14
      Hirai, T.; Yoshimatsu, I.; Yamaki, J. Effect of Additives on Lithium Cycling Efficiency. J. Electrochem. Soc. 1994, 141, 2300– 2305,  DOI: 10.1149/1.2055116
      14
      Effect of additives on lithium cycling efficiency
      Hirai, Toshiro; Yoshimatsu, Isamu; Yamaki, Jun-ichi
      Journal of the Electrochemical Society (1994), 141 (9), 2300-5CODEN: JESOAN; ISSN:0013-4651.
      Li cycling efficiency was evaluated for LiAsF6-ethylene carbonate/2-methyltetrahydrofuran mixed-solvent electrolyte (LiAsF6-EC/2MeTHF) contg. additives of tetraalkylammonium chlorides with a long n-alkyl chain and three Me groups. The tetraalkylammonium chloride with n-alkyl group longer than n-C12H25 increased Li cycling efficiency. Cetyltrimethylammonium chloride (CTAC) produced the best improvement in Li cycling efficiency. A figure of merit (FOM) of Li for 0.01M CTAC was 46, which was 1.5 times the FOM for the corresponding additive-free electrolyte. The LiAsF6-EC/2MeTHF with CTAC showed an increase in FOM with stack pressure, but the effect was less than that for the additive-free LiAsF6-EC/2MeTHF. SEM observation showed that the addn. of CTAC decreased the needle-like Li deposition and increased particulate Li deposition. This deposition morphol. may be the main cause of the increase in FOM. The additive had no effect on rate capability for cell cycling at 3 mA/cm2 discharge and 1 mA/cm2 charge.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2cXmtlCls7o%253D&md5=9125cb1237f464f238f6507a83b742ac
    15. 15
      Ding, F.; Xu, W.; Graff, G. L.; Zhang, J.; Sushko, M. L.; Chen, X.; Shao, Y.; Engelhard, M. H.; Nie, Z.; Xiao, J.; Liu, X.; Sushko, P. V.; Liu, J.; Zhang, J.-G. Dendrite-Free Lithium Deposition via Self-Healing Electrostatic Shield Mechanism. J. Am. Chem. Soc. 2013, 135, 4450– 4456,  DOI: 10.1021/ja312241y
      15
      Dendrite-Free Lithium Deposition via Self-Healing Electrostatic Shield Mechanism
      Ding, Fei; Xu, Wu; Graff, Gordon L.; Zhang, Jian; Sushko, Maria L.; Chen, Xilin; Shao, Yuyan; Engelhard, Mark H.; Nie, Zimin; Xiao, Jie; Liu, Xingjiang; Sushko, Peter V.; Liu, Jun; Zhang, Ji-Guang
      Journal of the American Chemical Society (2013), 135 (11), 4450-4456CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
      Rechargeable Li metal batteries are of great importance. Unfortunately, uncontrollable dendritic Li growth inherent in these batteries (upon repeated charge/discharge cycling) has prevented their practical application over the past 40 years. The authors show a novel mechanism that can fundamentally alter dendrite formation. At low concns., selected cations (such as Cs or Rb ions) exhibit an effective redn. potential below the std. redn. potential of Li ions. During Li deposition, these additive cations form a pos. charged electrostatic shield around the initial growth tip of the protuberances without redn. and deposition of the additives. This forces further deposition of Li to adjacent regions of the anode and eliminates dendrite formation in Li metal batteries. This strategy may also prevent dendrite growth in Li-ion batteries as well as other metal batteries and transform the surface uniformity of coatings deposited in many general electrodeposition processes.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXjtlWgsLs%253D&md5=be89c1fc31e4b3293c7f0fb4e6b55407
    16. 16
      Qian, J.; Henderson, W. A.; Xu, W.; Bhattacharya, P.; Engelhard, M.; Borodin, O.; Zhang, J.-G. High rate and stable cycling of lithium metal anode. Nat. Commun. 2015, 6, 6362,  DOI: 10.1038/ncomms7362
      16
      High rate and stable cycling of lithium metal anode
      Qian, Jiangfeng; Henderson, Wesley A.; Xu, Wu; Bhattacharya, Priyanka; Engelhard, Mark; Borodin, Oleg; Zhang, Ji-Guang
      Nature Communications (2015), 6 (), 6362CODEN: NCAOBW; ISSN:2041-1723. (Nature Publishing Group)
      Lithium metal is an ideal battery anode. However, dendrite growth and limited Coulombic efficiency during cycling have prevented its practical application in rechargeable batteries. Herein, we report that the use of highly concd. electrolytes composed of ether solvents and the lithium bis(fluorosulfonyl)imide salt enables the high-rate cycling of a lithium metal anode at high Coulombic efficiency (up to 99.1%) without dendrite growth. With 4 M lithium bis(fluorosulfonyl)imide in 1,2-dimethoxyethane as the electrolyte, a lithium|lithium cell can be cycled at 10 mA cm-2 for more than 6,000 cycles, and a copper|lithium cell can be cycled at 4 mA cm-2 for more than 1,000 cycles with an av. Coulombic efficiency of 98.4%. These excellent performances can be attributed to the increased solvent coordination and increased availability of lithium ion concn. in the electrolyte. Further development of this electrolyte may enable practical applications for lithium metal anode in rechargeable batteries.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtF2itrrI&md5=9bde3388626c7f3d31618a2d934a90cf
    17. 17
      Suo, L.; Hu, Y.-S.; Li, H.; Armand, M.; Chen, L. A new class of Solvent-in-Salt electrolyte for high-energy rechargeable metallic lithium batteries. Nat. Commun. 2013, 4, 1481,  DOI: 10.1038/ncomms2513
      17
      A new class of Solvent-in-Salt electrolyte for high-energy rechargeable metallic lithium batteries
      Suo Liumin; Hu Yong-Sheng; Li Hong; Armand Michel; Chen Liquan
      Nature communications (2013), 4 (), 1481 ISSN:.
      Liquid electrolyte plays a key role in commercial lithium-ion batteries to allow conduction of lithium-ion between cathode and anode. Traditionally, taking into account the ionic conductivity, viscosity and dissolubility of lithium salt, the salt concentration in liquid electrolytes is typically less than 1.2 mol l(-1). Here we show a new class of 'Solvent-in-Salt' electrolyte with ultrahigh salt concentration and high lithium-ion transference number (0.73), in which salt holds a dominant position in the lithium-ion transport system. It remarkably enhances cyclic and safety performance of next-generation high-energy rechargeable lithium batteries via an effective suppression of lithium dendrite growth and shape change in the metallic lithium anode. Moreover, when used in lithium-sulphur battery, the advantage of this electrolyte is further demonstrated that lithium polysulphide dissolution is inhibited, thus overcoming one of today's most challenging technological hurdles, the 'polysulphide shuttle phenomenon'. Consequently, a coulombic efficiency nearing 100% and long cycling stability are achieved.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3sznslKgtA%253D%253D&md5=cb719ace996906c08eadedf61644e3dc
    18. 18
      Lu, Y.; Tu, Z.; Archer, L. A. Stable lithium electrodeposition in liquid and nanoporous solid electrolytes. Nat. Mater. 2014, 13, 961,  DOI: 10.1038/nmat4041
      18
      Stable lithium electrodeposition in liquid and nanoporous solid electrolytes
      Lu, Yingying; Tu, Zhengyuan; Archer, Lynden A.
      Nature Materials (2014), 13 (10), 961-969CODEN: NMAACR; ISSN:1476-1122. (Nature Publishing Group)
      Rechargeable lithium, sodium and aluminum metal-based batteries are among the most versatile platforms for high-energy, cost-effective electrochem. energy storage. Non-uniform metal deposition and dendrite formation on the neg. electrode during repeated cycles of charge and discharge are major hurdles to commercialization of energy-storage devices based on each of these chemistries. A long-held view is that unstable electrodeposition is a consequence of inherent characteristics of these metals and their inability to form uniform electrodeposits on surfaces with inevitable defects. We report on electrodeposition of lithium in simple liq. electrolytes and in nanoporous solids infused with liq. electrolytes. We find that simple liq. electrolytes reinforced with halogenated salt blends exhibit stable long-term cycling at room temp., often with no signs of deposition instabilities over hundreds of cycles of charge and discharge and thousands of operating hours. We rationalize these observations with the help of surface energy data for the electrolyte/lithium interface and impedance anal. of the interface during different stages of cell operation. Our findings provide support for an important recent theor. prediction that the surface mobility of lithium is significantly enhanced in the presence of lithium halide salts. Our results also show that a high electrolyte modulus is unnecessary for stable electrodeposition of lithium.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhtlahu77J&md5=72567640c740e4e4b584b1e838d4d9d8
    19. 19
      Zhang, X.; Cheng, X.; Chen, X.; Yan, C.; Zhang, Q. Fluoroethylene Carbonate Additives to Render Uniform Li Deposits in Lithium Metal Batteries. Adv. Funct. Mater. 2017, 27, 1605989,  DOI: 10.1002/adfm.201605989
      There is no corresponding record for this reference.
    20. 20
      Wang, D.; Zhang, W.; Zheng, W.; Cui, X.; Rojo, T.; Zhang, Q. Towards High-Safe Lithium Metal Anodes: Suppressing Lithium Dendrites via Tuning Surface Energy. Adv. Sci. 2017, 4, 1600168,  DOI: 10.1002/advs.201600168
      20
      Towards High-Safe Lithium Metal Anodes: Suppressing Lithium Dendrites via Tuning Surface Energy
      Wang Dong; Zheng Weitao; Cui Xiaoqiang; Zhang Wei; Rojo Teofilo; Zhang Qiang
      Advanced science (Weinheim, Baden-Wurttemberg, Germany) (2017), 4 (1), 1600168 ISSN:2198-3844.
      The formation of lithium dendrites induces the notorious safety issue and poor cycling life of energy storage devices, such as lithium-sulfur and lithium-air batteries. We propose a surface energy model to describe the complex interface between the lithium anode and electrolyte. A universal strategy of hindering formation of lithium dendrites via tuning surface energy of the relevant thin film growth is suggested. The merit of the novel motif lies not only fundamentally a perfect correlation between electrochemistry and thin film fields, but also significantly promotes larger-scale application of lithium-sulfur and lithium-air batteries, as well as other metal batteries (e.g., Zn, Na, K, Cu, Ag, and Sn).
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC1c7nsFektg%253D%253D&md5=97708cfbe6ea7cd34edab1a667521da1
    21. 21
      Zhang, Y.; Qian, J.; Xu, W.; Russell, S. M.; Chen, X.; Nasybulin, E.; Bhattacharya, P.; Engelhard, M. H.; Mei, D.; Cao, R.; Ding, F.; Cresce, A. V.; Xu, K.; Zhang, J.-G. Dendrite-Free Lithium Deposition with Self-Aligned Nanorod Structure. Nano Lett. 2014, 14, 6889– 6896,  DOI: 10.1021/nl5039117
      21
      Dendrite-Free Lithium Deposition with Self-Aligned Nanorod Structure
      Zhang, Yaohui; Qian, Jiangfeng; Xu, Wu; Russell, Selena M.; Chen, Xilin; Nasybulin, Eduard; Bhattacharya, Priyanka; Engelhard, Mark H.; Mei, Donghai; Cao, Ruiguo; Ding, Fei; Cresce, Arthur V.; Xu, Kang; Zhang, Ji-Guang
      Nano Letters (2014), 14 (12), 6889-6896CODEN: NALEFD; ISSN:1530-6984. (American Chemical Society)
      Suppressing Li dendrite growth is one of the most crit. challenges for the development of Li metal batteries. Here, the authors report for the 1st time the growth of dendrite-free Li films with a self-aligned and highly compacted nanorod structure when the film was deposited in the electrolyte consisting of 1.0M LiPF6 in propylene carbonate with 0.05M CsPF6 as an additive. Evolution of both the surface and the cross-sectional morphologies of the Li films during repeated Li deposition/stripping processes were studied. The formation of the compact Li nanorod structure is preceded by a solid electrolyte interphase (SEI) layer formed on the surface of the substrate. Electrochem. anal. indicates that an initial redn. process occurred at ∼2.05 V vs. Li/Li+ before Li deposition is responsible for the formation of the initial SEI, while the XPS indicates that the presence of CsPF6 additive can largely enhance the formation of LiF in this initial SEI. Hence, the smooth Li deposition in Cs+-contg. electrolyte is the result of a synergistic effect of Cs+ additive and preformed SEI layer. A fundamental understanding on the compn., internal structure, and evolution of Li metal films may lead to new approaches to stabilize the long-term cycling stability of Li metal and other metal anodes for energy storage applications.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhvFOjs7zN&md5=568690f8c450f7a6ff90166875481674
    22. 22
      Mayers, M. Z.; Kaminski, J. W.; Miller, T. F. Suppression of Dendrite Formation via Pulse Charging in Rechargeable Lithium Metal Batteries. J. Phys. Chem. C 2012, 116, 26214– 26221,  DOI: 10.1021/jp309321w
      22
      Suppression of Dendrite Formation via Pulse Charging in Rechargeable Lithium Metal Batteries
      Mayers, Matthew Z.; Kaminski, Jakub W.; Miller, Thomas F.
      Journal of Physical Chemistry C (2012), 116 (50), 26214-26221CODEN: JPCCCK; ISSN:1932-7447. (American Chemical Society)
      We introduce a coarse-grained simulation model for the reductive deposition of lithium cations in secondary lithium batteries. The model accounts for the heterogeneous and nonequil. nature of the electrodeposition dynamics, and it enables simulation of the long timescales and lengthscales assocd. with metal dendrite formation. We investigate the effects of applied overpotential and material properties on early-stage dendrite formation, as well as the mol. mechanisms that govern this process. The model confirms that dendrite formation propensity increases with the applied electrode overpotential, and it demonstrates that application of the electrode overpotential in time-dependent pulses leads to dramatic suppression of dendrite formation while reducing the accumulated electrode on-time by as much as 96%. Moreover, the model predicts that time dependence of the applied electrode overpotential can lead to pos., neg., or zero correlation between cation diffusivity in the solid-electrolyte interphase (SEI) and dendrite formation propensity. Anal. of the simulation trajectories reveals that dendrite formation emerges from a competition between the timescales for cation diffusion and redn. at the anode/SEI interface, with lower applied overpotentials and shorter electrode pulse durations shifting this competition in favor of lower dendrite formation propensity. This work provides a mol. basis for understanding and designing pulsing waveforms that mitigate dendrite formation while minimally affecting battery charging times.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38Xhs12mtrzE&md5=e17f74971b90114acb68bb36063c52ce
    23. 23
      Aryanfar, A.; Brooks, D.; Merinov, B. V.; Goddard, W. A.; Colussi, A. J.; Hoffmann, M. R. Dynamics of Lithium Dendrite Growth and Inhibition: Pulse Charging Experiments and Monte Carlo Calculations. J. Phys. Chem. Lett. 2014, 5, 1721– 1726,  DOI: 10.1021/jz500207a
      23
      Dynamics of Lithium Dendrite Growth and Inhibition: Pulse Charging Experiments and Monte Carlo Calculations
      Aryanfar, Asghar; Brooks, Daniel; Merinov, Boris V.; Goddard, William A.; Colussi, Agustin J.; Hoffmann, Michael R.
      Journal of Physical Chemistry Letters (2014), 5 (10), 1721-1726CODEN: JPCLCD; ISSN:1948-7185. (American Chemical Society)
      Short-circuiting via dendrites compromises the reliability of Li-metal batteries. Dendrites ensue from instabilities inherent to electrodeposition that should be amenable to dynamic control. Here, the authors report that by charging a scaled coin-cell prototype with 1 ms pulses followed by 3 ms rest periods the av. dendrite length is shortened ∼2.5 times relative to those grown under continuous charging. Monte Carlo simulations dealing with Li+ diffusion and electromigration reveal that expts. involving 20 ms pulses were ineffective because Li+ migration in the strong elec. fields converging to dendrite tips generates extended depleted layers that cannot be replenished by diffusion during rest periods. Because the application of pulses much shorter than the characteristic time τc approx. O(∼1 ms) for polarizing elec. double layers in the system would approach d.c. charging, probably dendrite propagation can should be inhibited (albeit not suppressed) by pulse charging within appropriate frequency ranges.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXntFOmurw%253D&md5=a49ca2f7ec9d6e627017e04994a96429
    24. 24
      Liu, Q.; Xu, J.; Yuan, S.; Chang, Z.; Xu, D.; Yin, Y.; Li, L.; Zhong, H.; Jiang, Y.; Yan, J.; Zhang, X. Artificial Protection Film on Lithium Metal Anode toward Long-Cycle-Life Lithium-Oxygen Batteries. Adv. Mater. 2015, 27, 5241– 5247,  DOI: 10.1002/adma.201501490
      24
      Artificial Protection Film on Lithium Metal Anode toward Long-Cycle-Life Lithium-Oxygen Batteries
      Liu, Qing-Chao; Xu, Ji-Jing; Yuan, Shuang; Chang, Zhi-Wen; Xu, Dan; Yin, Yan-Bin; Li, Lin; Zhong, Hai-Xia; Jiang, Yin-Shan; Yan, Jun-Min; Zhang, Xin-Bo
      Advanced Materials (Weinheim, Germany) (2015), 27 (35), 5241-5247CODEN: ADVMEW; ISSN:0935-9648. (Wiley-VCH Verlag GmbH & Co. KGaA)
      A facile and effective strategy was developed to protect the lithium anode of a secondary lithium battery through fabrication of a protection film on the metal Li anode, in which a fluoroethylene carbonate (I) additive plays a key role in the crucial film-forming additive. As a proof-of-concept expt., even when using conventional Super P (carbon black) cathode, the obtained I-treated Li metal anode endowed Li-O2 batteries with superior cycle stability of >100 stable cycles with a fixed capacity of 1000 mA-h/g at a c.d. of 300 mA/g was obtained, which is more than three times that of the cells with a pristine Li metal anode and Li metal anode treated without I. The significantly improved cycling stability could be attributed to the protective film derived from I decompn.,.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtlWgsL%252FN&md5=e7d030229d8bda40d111e25dbce59847
    25. 25
      Yan, K.; Lee, H.-W.; Gao, T.; Zheng, G.; Yao, H.; Wang, H.; Lu, Z.; Zhou, Y.; Liang, Z.; Liu, Z.; Chu, S.; Cui, Y. Ultrathin Two-Dimensional Atomic Crystals as Stable Interfacial Layer for Improvement of Lithium Metal Anode. Nano Lett. 2014, 14, 6016– 6022,  DOI: 10.1021/nl503125u
      25
      Ultrathin Two-Dimensional Atomic Crystals as Stable Interfacial Layer for Improvement of Lithium Metal Anode
      Yan, Kai; Lee, Hyun-Wook; Gao, Teng; Zheng, Guangyuan; Yao, Hongbin; Wang, Haotian; Lu, Zhenda; Zhou, Yu; Liang, Zheng; Liu, Zhongfan; Chu, Steven; Cui, Yi
      Nano Letters (2014), 14 (10), 6016-6022CODEN: NALEFD; ISSN:1530-6984. (American Chemical Society)
      Stable cycling of lithium metal anode is challenging due to the dendritic lithium formation and high chem. reactivity of lithium with electrolyte and nearly all the materials. Here, we demonstrate a promising novel electrode design by growing two-dimensional (2D) at. crystal layers including hexagonal boron nitride (h-BN) and graphene directly on Cu metal current collectors. Lithium ions were able to penetrate through the point and line defects of the 2D layers during the electrochem. deposition, leading to sandwiched lithium metal between ultrathin 2D layers and Cu. The 2D layers afford an excellent interfacial protection of Li metal due to their remarkable chem. stability as well as mech. strength and flexibility, resulting from the strong intralayer bonds and ultrathin thickness. Smooth Li metal deposition without dendritic and mossy Li formation was realized. We showed stable cycling over 50 cycles with Coulombic efficiency ∼97% in org. carbonate electrolyte with c.d. and areal capacity up to the practical value of 2.0 mA/cm2and 5.0 mAh/cm2, resp., which is a significant improvement over the unprotected electrodes in the same electrolyte.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsVekt7jJ&md5=b7cf8adb44da1aedf75c2a104d259fb2
    26. 26
      Liu, Y.; Lin, D.; Yuen, P. Y.; Liu, K.; Xie, J.; Dauskardt, R. H.; Cui, Y. An Artificial Solid Electrolyte Interphase with High Li-Ion Conductivity, Mechanical Strength, and Flexibility for Stable Lithium Metal Anodes. Adv. Mater. 2017, 29, 1605531,  DOI: 10.1002/adma.201605531
      There is no corresponding record for this reference.
    27. 27
      Khurana, R.; Schaefer, J. L.; Archer, L. A.; Coates, G. W. Suppression of Lithium Dendrite Growth Using Cross-Linked Polyethylene/Poly(ethylene oxide) Electrolytes: A New Approach for Practical Lithium-Metal Polymer Batteries. J. Am. Chem. Soc. 2014, 136, 7395– 7402,  DOI: 10.1021/ja502133j
      27
      Suppression of Lithium Dendrite Growth Using Cross-Linked Polyethylene/Poly(ethylene oxide) Electrolytes: A New Approach for Practical Lithium-Metal Polymer Batteries
      Khurana, Rachna; Schaefer, Jennifer L.; Archer, Lynden A.; Coates, Geoffrey W.
      Journal of the American Chemical Society (2014), 136 (20), 7395-7402CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
      Solid polymer electrolyte (SPE) membranes are a crit. component of high specific energy rechargeable Li-metal polymer (LMP) batteries. SPEs exhibit low volatility and thus increase the safety of Li-based batteries compared to current state-of-the-art Li-ion batteries that use flammable small-mol. electrolytes. However, most SPEs exhibit low ionic cond. at room temp., and often allow the growth of lithium dendrites that short-circuit the batteries. Both of these deficiencies are significant barriers to the commercialization of LMP batteries. Herein a cross-linked polyethylene/poly(ethylene oxide) SPE is reported with both high ionic cond. (> 1.0 × 10-4 S/cm at 25°) and excellent resistance to dendrite growth. It has been proposed that SPEs with shear moduli of the same order of magnitude as lithium could be used to suppress dendrite growth, leading to increased lifetime and safety for LMP batteries. In contrast to the theor. predictions, the low-modulus (G' ≈ 1.0 × 105 Pa at 90°) cross-linked SPEs reported herein exhibit remarkable dendrite growth resistance. These results suggest that a high-modulus SPE is not a requirement for the control of dendrite proliferation.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXmsFWnsrc%253D&md5=71960add56cf11e7e9d858a50b0c6d1c
    28. 28
      Stone, G. M.; Mullin, S. A.; Teran, A. A.; Hallinan, D. T.; Minor, A. M.; Hexemer, A.; Balsara, N. P. Resolution of the Modulus versus Adhesion Dilemma in Solid Polymer Electrolytes for Rechargeable Lithium Metal Batteries. J. Electrochem. Soc. 2012, 159, A222– A227,  DOI: 10.1149/2.030203jes
      28
      Resolution of the Modulus versus Adhesion Dilemma in Solid Polymer Electrolytes for Rechargeable Lithium Metal Batteries
      Stone, G. M.; Mullin, S. A.; Teran, A. A.; Hallinan, D. T., Jr.; Minor, A. M.; Hexemer, A.; Balsara, N. P.
      Journal of the Electrochemical Society (2012), 159 (3), A222-A227CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
      The authors present a solid electrolyte that adheres to the Li surface and resists dendrite growth, both of which are needed for the development of high specific energy rechargeable batteries with Li anodes. Nanostructured lamellar block copolymer electrolytes exhibit solid-like properties in the bulk, due to the presence of a randomly oriented granular structure, and liq.-like surface properties due to the formation of perpendicularly oriented lamellae at the Li-electrolyte interface. The amt. of charge that can be passed before short-circuit in a sym. Li-polymer-Li cell with nanostructured polystyrene-block-poly(ethylene oxide) electrolytes is larger than that obtained with homopolymer poly(ethylene oxide) electrolytes by a factor of 11 to 48. Grazing incident small angle x-ray scattering confirms that the microstructure of the block copolymer near the Li-polymer interface has a perpendicular orientation. This orientation leads to a liq.-like behavior of the polymer at the interface due to the liq. cryst. symmetry of block copolymers. This combination of bulk and surface properties enhances the resistance to dendrites while maintaining electrode-electrolyte contact.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XnvVSnsg%253D%253D&md5=45d31f648114ce418df69b70a8b1ab72
    29. 29
      Yue, L.; Ma, J.; Zhang, J.; Zhao, J.; Dong, S.; Liu, Z.; Cui, G.; Chen, L. All solid-state polymer electrolytes for high-performance lithium ion batteries. Energy Storage Mater. 2016, 5, 139– 164,  DOI: 10.1016/j.ensm.2016.07.003
      There is no corresponding record for this reference.
    30. 30
      Janek, J.; Zeier, W. G. A solid future for battery development. Nat. Energy 2016, 1, 16141,  DOI: 10.1038/nenergy.2016.141
      There is no corresponding record for this reference.
    31. 31
      Li, J.; Ma, C.; Chi, M.; Liang, C.; Dudney, N. J. Solid Electrolyte: the Key for High-Voltage Lithium Batteries. Adv. Energy Mater. 2015, 5, 1401408,  DOI: 10.1002/aenm.201401408
      There is no corresponding record for this reference.
    32. 32
      Suzuki, Y.; Kami, K.; Watanabe, K.; Watanabe, A.; Saito, N.; Ohnishi, T.; Takada, K.; Sudo, R.; Imanishi, N. Transparent cubic garnet-type solid electrolyte of Al2O3-doped Li7La3Zr2O12. Solid State Ionics 2015, 278, 172– 176,  DOI: 10.1016/j.ssi.2015.06.009
      32
      Transparent cubic garnet-type solid electrolyte of Al2O3-doped Li7La3Zr2O12
      Suzuki, Yosuke; Kami, K.; Watanabe, K.; Watanabe, A.; Saito, N.; Ohnishi, T.; Takada, K.; Sudo, R.; Imanishi, N.
      Solid State Ionics (2015), 278 (), 172-176CODEN: SSIOD3; ISSN:0167-2738. (Elsevier B.V.)
      A transparent garnet-type lithium-ion conducting solid electrolyte of 1.0 wt. % Al2O3-doped Li7La3Zr2O12 (A-LLZ) was prepd. using hot isostatic pressing (HIP). The A-LLZ pellet sintered at 1180°C for 36 h was followed by HIP treatment at 127 MPa and 1180°C under an Ar atm. The bulk cond. of the HIP treated A-LLZ was 9.9 × 10-4 S cm-1 at 25°C. The Li/HIP treated A-LLZ/Li cell showed no short-circuit due to lithium dendrite formation at 0.5 mA cm- 2.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtVKmsbrP&md5=7fd10b6e096b6cc875cc5495c59477c7
    33. 33
      Manthiram, A.; Yu, X.; Wang, S. Lithium battery chemistries enabled by solid-state electrolytes. Nat. Rev. Mater. 2017, 2, 16103,  DOI: 10.1038/natrevmats.2016.103
      33
      Lithium battery chemistries enabled by solid-state electrolytes
      Manthiram, Arumugam; Yu, Xingwen; Wang, Shaofei
      Nature Reviews Materials (2017), 2 (3), 16103CODEN: NRMADL; ISSN:2058-8437. (Nature Publishing Group)
      Solid-state electrolytes are attracting increasing interest for electrochem. energy storage technologies. In this Review, we provide a background overview and discuss the state of the art, ion-transport mechanisms and fundamental properties of solid-state electrolyte materials of interest for energy storage applications. We focus on recent advances in various classes of battery chemistries and systems that are enabled by solid electrolytes, including all-solid-state lithium-ion batteries and emerging solid-electrolyte lithium batteries that feature cathodes with liq. or gaseous active materials (for example, lithium-air, lithium-sulfur and lithium-bromine systems). A low-cost, safe, aq. electrochem. energy storage concept with a 'mediator-ion' solid electrolyte is also discussed. Advanced battery systems based on solid electrolytes would revitalize the rechargeable battery field because of their safety, excellent stability, long cycle lives and low cost. However, great effort will be needed to implement solid-electrolyte batteries as viable energy storage systems. In this context, we discuss the main issues that must be addressed, such as achieving acceptable ionic cond., electrochem. stability and mech. properties of the solid electrolytes, as well as a compatible electrolyte/electrode interface.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXislGitr0%253D&md5=be4704bc600127083842361f9e75c578
    34. 34
      Kamaya, N.; Homma, K.; Yamakawa, Y.; Hirayama, M.; Kanno, R.; Yonemura, M.; Kamiyama, T.; Kato, Y.; Hama, S.; Kawamoto, K.; Mitsui, A. A lithium superionic conductor. Nat. Mater. 2011, 10, 682– 686,  DOI: 10.1038/nmat3066
      34
      A lithium superionic conductor
      Kamaya, Noriaki; Homma, Kenji; Yamakawa, Yuichiro; Hirayama, Masaaki; Kanno, Ryoji; Yonemura, Masao; Kamiyama, Takashi; Kato, Yuki; Hama, Shigenori; Kawamoto, Koji; Mitsui, Akio
      Nature Materials (2011), 10 (9), 682-686CODEN: NMAACR; ISSN:1476-1122. (Nature Publishing Group)
      Batteries are a key technol. in modern society. They are used to power elec. and hybrid elec. vehicles and to store wind and solar energy in smart grids. Electrochem. devices with high energy and power densities can currently be powered only by batteries with org. liq. electrolytes. However, such batteries require relatively stringent safety precautions, making large-scale systems complicated and expensive. The application of solid electrolytes is currently limited because they attain practically useful conductivities (10-2 S/cm) only at 50-80°, which is one order of magnitude lower than those of org. liq. electrolytes. Here, the authors report a Li superionic conductor, Li10GeP2S12 that has a new 3-dimensional framework structure. It exhibits an extremely high Li ionic cond. of 12 mS/cm at room temp. This represents the highest cond. achieved in a solid electrolyte, exceeding even those of liq. org. electrolytes. This new solid-state battery electrolyte has many advantages in terms of device fabrication (facile shaping, patterning and integration), stability (non-volatile), safety (non-explosive) and excellent electrochem. properties (high cond. and wide potential window).
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3MXpsFaisLc%253D&md5=30637e2968f742a3e1da5f7fa4250644
    35. 35
      Kato, Y.; Hori, S.; Saito, T.; Suzuki, K.; Hirayama, M.; Mitsui, A.; Yonemura, M.; Iba, H.; Kanno, R. High-power all-solid-state batteries using sulfide superionic conductors. Nat. Energy 2016, 1, 16030,  DOI: 10.1038/nenergy.2016.30
      35
      High-power all-solid-state batteries using sulfide superionic conductors
      Kato, Yuki; Hori, Satoshi; Saito, Toshiya; Suzuki, Kota; Hirayama, Masaaki; Mitsui, Akio; Yonemura, Masao; Iba, Hideki; Kanno, Ryoji
      Nature Energy (2016), 1 (4), 16030CODEN: NEANFD; ISSN:2058-7546. (Nature Publishing Group)
      Compared with Li-ion batteries with liq. electrolytes, all-solid-state batteries offer an attractive option owing to their potential in improving the safety and achieving both high power and high energy densities. Despite extensive research efforts, the development of all-solid-state batteries still falls short of expectation largely because of the lack of suitable candidate materials for the electrolyte required for practical applications. Here the authors report Li superionic conductors with an exceptionally high cond. (25 mS cm-1 for Li9.54Si1.74P1.44S11.7Cl0.3), as well as high stability ( ∼0 V vs. Li metal for Li9.6P3S12). A fabricated all-solid-state cell based on this Li conductor has very small internal resistance, esp. at 100 oC. The cell possesses high specific power that is superior to that of conventional cells with liq. electrolytes. Stable cycling with a high c.d. of 18 C (charging/discharging in just 3 min; where C is the C-rate) is also demonstrated.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVekurs%253D&md5=cc1210221e70bc3e6c06e015effc70de
    36. 36
      Kerman, K.; Luntz, A.; Viswanathan, V.; Chiang, Y.-M.; Chen, Z. Review-Practical Challenges Hindering the Development of Solid State Li Ion Batteries. J. Electrochem. Soc. 2017, 164, A1731– A1744,  DOI: 10.1149/2.1571707jes
      36
      Review-Practical Challenges Hindering the Development of Solid State Li Ion Batteries
      Kerman, Kian; Luntz, Alan; Viswanathan, Venkatasubramanian; Chiang, Yet-Ming; Chen, Zhebo
      Journal of the Electrochemical Society (2017), 164 (7), A1731-A1744CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
      Solid state electrolyte systems boasting Li+ cond. of >10 mS cm-1 at room temp. have opened the potential for developing a solid state battery with power and energy densities that are competitive with conventional liq. electrolyte systems. The primary focus of this review is twofold. First, differences in Li penetration resistance in solid state systems are discussed, and kinetic limitations of the solid state interface are highlighted. Second, technol. challenges assocd. with processing such systems in relevant form factors are elucidated, and architectures needed for cell level devices in the context of product development are reviewed. Specific research vectors that provide high value to advancing solid state batteries are outlined and discussed.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVCitLvI&md5=99cf3345659e25c91eb25ed7090d1403
    37. 37
      Sharafi, A.; Yu, S.; Naguib, M.; Lee, M.; Ma, C.; Meyer, H. M.; Nanda, J.; Chi, M.; Siegel, D. J.; Sakamoto, J. Impact of air exposure and surface chemistry on Li-Li7La3Zr2O12 interfacial resistance. J. Mater. Chem. A 2017, 5, 13475– 13487,  DOI: 10.1039/C7TA03162A
      37
      Impact of air exposure and surface chemistry on Li-Li7La3Zr2O12 interfacial resistance
      Sharafi, Asma; Yu, Seungho; Naguib, Michael; Lee, Marcus; Ma, Cheng; Meyer, Harry M.; Nanda, Jagjit; Chi, Maiofang; Siegel, Donald J.; Sakamoto, Jeff
      Journal of Materials Chemistry A: Materials for Energy and Sustainability (2017), 5 (26), 13475-13487CODEN: JMCAET; ISSN:2050-7496. (Royal Society of Chemistry)
      Li7La3Zr2O12 (LLZO) is a promising solid-state electrolyte that could enable solid-state-batteries (SSB) using metallic Li anodes. For a SSB to be viable, the stability and charge transfer kinetics at the Li-LLZO interface should foster facile plating and stripping of Li. Contrary to these goals, recent studies have reported high Li-LLZO interfacial resistance which was attributed to a contamination layer that forms upon exposure of LLZO to air. This study clarifies the mechanisms and consequences assocd. with air exposure of LLZO; addnl., strategies to minimize these effects are described. First-principles calcns. reveal that LLZO readily reacts with humid air; the most favorable reaction pathway involves protonation of LLZO and formation of Li2CO3. XPS, SEM, Raman spectroscopy, and transmission electron microscopy were used to characterize the surface and subsurface chem. of LLZO as a function of relative humidity and exposure time. Electrochem. impedance spectroscopy was used to measure the Li-LLZO interfacial resistance as a function of surface contamination. These data indicate that air exposure-induced contamination impacts the interfacial resistance significantly, when exposure time exceeds 24 h. The results of this study provide valuable insight into the sensitivity of LLZO to air and how the effects of air contamination can be reversed.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtVWmtrnE&md5=ba7e873b21db6b50f59e450c3d6f6ec2
    38. 38
      Sharafi, A.; Haslam, C. G.; Kerns, R. D.; Wolfenstine, J.; Sakamoto, J. Controlling and correlating the effect of grain size with the mechanical and electrochemical properties of Li7La3Zr2O12 solid-state electrolyte. J. Mater. Chem. A 2017, 5, 21491– 21504,  DOI: 10.1039/C7TA06790A
      38
      Controlling and correlating the effect of grain size with the mechanical and electrochemical properties of Li7La3Zr2O12 solid-state electrolyte
      Sharafi, Asma; Haslam, Catherine G.; Kerns, Robert D.; Wolfenstine, Jeff; Sakamoto, Jeff
      Journal of Materials Chemistry A: Materials for Energy and Sustainability (2017), 5 (40), 21491-21504CODEN: JMCAET; ISSN:2050-7496. (Royal Society of Chemistry)
      Li7La3Zr2O12 (LLZO) solid-state electrolyte is garnering interest due to its potential to enable solid-state batteries (SSBs) using metallic Li anodes. However, Li metal propagates along LLZO grain boundaries at high Li plating current densities (above the crit. c.d., CCD). In the present study, we examd. whether microstructural aspects, such as grain size, could influence mech. and electrochem. properties thereby affecting the CCD. A unique densification technique (heating between 1100 and 1300 °C) was used to control grain size. Electron backscatter diffraction detd. that the grain size and the misorientation angle varied from 5 to 600 μm and 20 to 40°, resp. Vickers indentation was used to characterize the mech. properties and revealed that hardness decreased (9.9-6.8 GPa) with increasing grain size, but the fracture toughness was invariant (0.6 MPa m-1/2) at grain sizes ≥40 μm. DC and AC techniques were used to measure and correlate the CCD with grain size and showed that the CCD increased with increasing grain size achieving a max. of 0.6 mA cm-2. We believe the implications of this work could be far-reaching in that they represent a significant step towards understanding the mechanism(s) that control the stability of the Li-LLZO interface and a rational approach to increase the CCD in SSBs.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhs1Sksb%252FP&md5=eccb1f1d8cfb3d13b2237d27793aa772
    39. 39
      Monroe, C.; Newman, J. The Impact of Elastic Deformation on Deposition Kinetics at Lithium/Polymer Interfaces. J. Electrochem. Soc. 2005, 152, A396– A404,  DOI: 10.1149/1.1850854
      39
      The impact of elastic deformation on deposition kinetics at lithium/polymer interfaces
      Monroe, Charles; Newman, John
      Journal of the Electrochemical Society (2005), 152 (2), A396-A404CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
      Past theories of electrode stability assume that the surface tension resists the amplification of surface roughness at cathodes and show that instability at lithium/liq. interfaces cannot be prevented by surface forces alone. This work treats interfacial stability in lithium/polymer systems where the electrolyte is solid. Linear elasticity theory is employed to compute the addnl. effect of bulk mech. forces on electrode stability. The lithium and polymer are treated as Hookean elastic materials, characterized by their shear moduli and Poisson's ratios. Two-dimensional displacement distributions that satisfy force balances across a periodically deforming interface are derived; these allow computation of the stress and surface-tension forces. The incorporation of elastic effects into a kinetic model demonstrates regimes of electrolyte mech. properties where amplification of surface roughness can be inhibited. For a polymer material with Poisson's ratio similar to poly(ethylene oxide), interfacial roughening is mech. suppressed when the separator shear modulus is about twice that of lithium.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXhs1KktLc%253D&md5=e878820c6a811396757bd7435bdb40ad
    40. 40
      Ahmad, Z.; Viswanathan, V. Stability of Electrodeposition at Solid-Solid Interfaces and Implications for Metal Anodes. Phys. Rev. Lett. 2017, 119, 056003,  DOI: 10.1103/PhysRevLett.119.056003
      40
      Stability of electrodeposition at solid-solid interfaces and implications for metal anodes
      Ahmad, Zeeshan; Viswanathan, Venkatasubramanian
      Physical Review Letters (2017), 119 (5), 056003/1-056003/6CODEN: PRLTAO; ISSN:1079-7114. (American Physical Society)
      We generalize the conditions for stable electrodeposition at isotropic solid-solid interfaces using a kinetic model which incorporates the effects of stresses and surface tension at the interface. We develop a stability diagram that shows two regimes of stability: a previously known pressure-driven mechanism and a new d.-driven stability mechanism that is governed by the relative d. of metal in the two phases. We show that inorg. solids and solid polymers generally do not lead to stable electrodeposition, and provide design guidelines for achieving stable electrodeposition.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhs1SrtbrP&md5=b71331ba73f970ec5c45f52cdc08ecad
    41. 41
      Diggle, J. W.; Despic, A. R.; Bockris, J. O. The Mechanism of the Dendritic Electrocrystallization of Zinc. J. Electrochem. Soc. 1969, 116, 1503– 1514,  DOI: 10.1149/1.2411588
      41
      Mechanism of the dendritic electrocrystallization of zinc
      Diggle, J. W.; Despic, A. R.; Bockris, J. O'M.
      Journal of the Electrochemical Society (1969), 116 (11), 1503-14CODEN: JESOAN; ISSN:0013-4651.
      Measurements were made of the growth rate of Zn dendrites in alk. zincate solns. as a function of overpotential (η), concn. (c), and temp. (T). The tip radii were measured by electron microscopy. At const. potential, an initiation time of between 5 and 100 min is observed, depending on η, c, and T. The total current to base and dendrite was independent of time until a time *aui, where τi < τd (the time for initiation obtained from the growth rate vs. time relation). Thereafter, i is proportional to t2. A crit. overpotential was detd., -75 mv. >ηcrit> -85 mv. Below this ηcrit, sponge was formed. Dendrites were observed up to η = -160 mv.; above this the deposition was heavy sponge. At a given c, the growth rate of a given dendrite increased with η according to an exponential law. The growing tip is parabolic, where 10-5 < γtip < 10-4 cm. No twinning was observed. The basic model used depended on the increase in c.d. possible for an electrodic reaction when the diffusion current depends on a radius of curvature of the substrate, rather than the linear diffusion layer thickness, δ. When the tip of a dendrite-precursor attains this condition, its growth is released from the diffusion control characteristic of it in the predendrite situation, and it grows further under predominantly activation control at a rate far greater than that possible in any other direction, where the radii of curvature are much greater. The Gibbs radius-dependent overpotential term is also present, although it has a minimized influence. The initiation of the dendrite is treated in terms of growing pyramids on the substrate surface. At 1st the growth is linear-diffusion controlled, but the rotation of the spiral, within the linear diffusion boundary surrounding the sphere, gives rise to a decrease of the effective radius (γ) of curvature of the dendrite tip until the value γ<0.1 δ is attained, which is effectively the condition for the dendrite initiation. The theory of the propagation in terms of the activation, diffusion, and Gibbs overpotential is consistent, in terms of τd, with expt. A derived growth-time line is also numerically consistent with expt. The dendrite growth rate as a function of c and η are numerically calcd. with reasonable consistency. The tip radius can also be approx. calcd. in terms of the present model.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaE3cXos1yl&md5=48077f26ea20fa76b2cc78e39c4f1309
    42. 42
      Monroe, C.; Newman, J. Dendrite Growth in Lithium/Polymer Systems: A Propagation Model for Liquid Electrolytes under Galvanostatic Conditions. J. Electrochem. Soc. 2003, 150, A1377– A1384,  DOI: 10.1149/1.1606686
      42
      Dendrite Growth in Lithium/Polymer Systems
      Monroe, Charles; Newman, John
      Journal of the Electrochemical Society (2003), 150 (10), A1377-A1384CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
      Dendrite growth in a parallel-electrode Li/polymer battery during galvanostatic charging was modeled. The growth model is surface-energy controlled, incorporating the effect of dendrite tip curvature into the dendrite growth kinetics. Using data representative of the oxymethylene-linked poly(ethylene oxide)/LiTFSI system, dendrites accelerate across cells under all conditions, and growth is always slowed by lowering the c.d. Cell shorting occurs during typical charges at current densities >75% of the limiting current. Increased interelectrode distance slows failure, but the advantages decrease as distance increases. A factor of 1000 increase in surface forces delays cell failure by only 6%. While larger diffusion coeffs. usually extend the time to cell failure, this trend is not consistent at high transference nos.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3sXnt1OmtLw%253D&md5=178ec3a3e854d05aede7708d68c7957a
    43. 43
      Monroe, C.; Newman, J. The Effect of Interfacial Deformation on Electrodeposition Kinetics. J. Electrochem. Soc. 2004, 151, A880– A886,  DOI: 10.1149/1.1710893
      43
      The Effect of Interfacial Deformation on Electrodeposition Kinetics
      Monroe, Charles; Newman, John
      Journal of the Electrochemical Society (2004), 151 (6), A880-A886CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
      Mullins-Sekerka linear stability anal. and the Barton and Bockris dendrite-propagation model are popular methods used to describe cathodic roughening and dendritic growth. These commonly cited theories employ kinetic relations that differ in math. form, but both contain the effects of surface tension and local concn. deviations induced by surface roughening. Here, a kinetic model is developed which addnl. includes mech. forces such as elasticity, viscous drag, and pressure, showing their effect on exchange current densities and potentials at roughening interfaces. The proposed expression describes the c.d. in terms of applied overpotential at deformed interfaces with arbitrary three-dimensional interfacial geometry. Both the Mullins-Sekerka and the Barton-Bockris kinetics can be derived as special cases of the general expression, thereby validating the proposed model and elucidating the fundamental assumptions on which the 2 previous theories rely.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXktVGkt70%253D&md5=22ed18c5cf280522d5d26e2bc30cb6bc
    44. 44
      Curtarolo, S.; Hart, G. L. W.; Nardelli, M. B.; Mingo, N.; Sanvito, S.; Levy, O. The high-throughput highway to computational materials design. Nat. Mater. 2013, 12, 191,  DOI: 10.1038/nmat3568
      44
      The high-throughput highway to computational materials design
      Curtarolo, Stefano; Hart, Gus L. W.; Nardelli, Marco Buongiorno; Mingo, Natalio; Sanvito, Stefano; Levy, Ohad
      Nature Materials (2013), 12 (3), 191-201CODEN: NMAACR; ISSN:1476-1122. (Nature Publishing Group)
      A review. High-throughput computational materials design is an emerging area of materials science. By combining advanced thermodn. and electronic-structure methods with intelligent data mining and database construction, and exploiting the power of current supercomputer architectures, scientists generate, manage and analyze enormous data repositories for the discovery of novel materials. In this Review we provide a current snapshot of this rapidly evolving field, and highlight the challenges and opportunities that lie ahead.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXislWju7c%253D&md5=5e116fbafda8e8437ccd0fdf7304d939
    45. 45
      Saal, J. E.; Kirklin, S.; Aykol, M.; Meredig, B.; Wolverton, C. Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD). JOM 2013, 65, 1501– 1509,  DOI: 10.1007/s11837-013-0755-4
      45
      Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD)
      Saal, James E.; Kirklin, Scott; Aykol, Muratahan; Meredig, Bryce; Wolverton, C.
      JOM (2013), 65 (11), 1501-1509CODEN: JOMMER; ISSN:1047-4838. (Springer)
      A review. High-throughput d. functional theory (HT DFT) is fast becoming a powerful tool for accelerating materials design and discovery by the amassing tens and even hundreds of thousands of DFT calcns. in large databases. Complex materials problems can be approached much more efficiently and broadly through the sheer quantity of structures and chemistries available in such databases. Our HT DFT database, the Open Quantum Materials Database (OQMD), contains over 200,000 DFT calcd. crystal structures and will be freely available for public use at http://oqmd.org. In this review, we describe the OQMD and its use in five materials problems, spanning a wide range of applications and materials types: (I) Li-air battery combination catalyst/electrodes, (II) Li-ion battery anodes, (III) Li-ion battery cathode coatings reactive with HF, (IV) Mg-alloy long-period stacking ordered (LPSO) strengthening ppts., and (V) training a machine learning model to predict new stable ternary compds.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsFentbzK&md5=feaac43dc6ff4c2d7a7a4c94f0b58c2b
    46. 46
      Pilania, G.; Wang, C.; Jiang, X.; Rajasekaran, S.; Ramprasad, R. Accelerating materials property predictions using machine learning. Sci. Rep. 2013, 3, 2810,  DOI: 10.1038/srep02810
      46
      Accelerating materials property predictions using machine learning
      Pilania Ghanshyam; Wang Chenchen; Jiang Xun; Rajasekaran Sanguthevar; Ramprasad Ramamurthy
      Scientific reports (2013), 3 (), 2810 ISSN:.
      The materials discovery process can be significantly expedited and simplified if we can learn effectively from available knowledge and data. In the present contribution, we show that efficient and accurate prediction of a diverse set of properties of material systems is possible by employing machine (or statistical) learning methods trained on quantum mechanical computations in combination with the notions of chemical similarity. Using a family of one-dimensional chain systems, we present a general formalism that allows us to discover decision rules that establish a mapping between easily accessible attributes of a system and its properties. It is shown that fingerprints based on either chemo-structural (compositional and configurational information) or the electronic charge density distribution can be used to make ultra-fast, yet accurate, property predictions. Harnessing such learning paradigms extends recent efforts to systematically explore and mine vast chemical spaces, and can significantly accelerate the discovery of new application-specific materials.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2c%252FltlCqtQ%253D%253D&md5=3475edb4dda3d93baae0d552d482b187
    47. 47
      Liu, Y.; Zhao, T.; Ju, W.; Shi, S. Materials discovery and design using machine learning. J. Materiomics 2017, 3, 159– 177,  DOI: 10.1016/j.jmat.2017.08.002
      There is no corresponding record for this reference.
    48. 48
      Gómez-Bombarelli, R.; Wei, J. N.; Duvenaud, D.; Hernández-Lobato, J. M.; Sánchez-Lengeling, B.; Sheberla, D.; Aguilera-Iparraguirre, J.; Hirzel, T. D.; Adams, R. P.; Aspuru-Guzik, A. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Cent. Sci. 2018, 4, 268– 276,  DOI: 10.1021/acscentsci.7b00572
      48
      Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
      Gomez-Bombarelli, Rafael; Wei, Jennifer N.; Duvenaud, David; Hernandez-Lobato, Jose Miguel; Sanchez-Lengeling, Benjamin; Sheberla, Dennis; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D.; Adams, Ryan P.; Aspuru-Guzik, Alan
      ACS Central Science (2018), 4 (2), 268-276CODEN: ACSCII; ISSN:2374-7951. (American Chemical Society)
      We report a method to convert discrete representations of mols. to and from a multidimensional continuous representation. This model allows us to generate new mols. for efficient exploration and optimization through open-ended spaces of chem. compds. A deep neural network was trained on hundreds of thousands of existing chem. structures to construct three coupled functions: an encoder, a decoder, and a predictor. The encoder converts the discrete representation of a mol. into a real-valued continuous vector, and the decoder converts these continuous vectors back to discrete mol. representations. The predictor ests. chem. properties from the latent continuous vector representation of the mol. Continuous representations of mols. allow us to automatically generate novel chem. structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chem. structures, or interpolating between mols. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compds. We demonstrate our method in the domain of drug-like mols. and also in a set of mols. with fewer that nine heavy atoms.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXntlWquw%253D%253D&md5=322d9ff569fc9c8831e91d915104d985
    49. 49
      de Jong, M.; Chen, W.; Notestine, R.; Persson, K.; Ceder, G.; Jain, A.; Asta, M.; Gamst, A. A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds. Sci. Rep. 2016, 6, 34256,  DOI: 10.1038/srep34256
      49
      A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds
      de Jong, Maarten; Chen, Wei; Notestine, Randy; Persson, Kristin; Ceder, Gerbrand; Jain, Anubhav; Asta, Mark; Gamst, Anthony
      Scientific Reports (2016), 6 (), 34256CODEN: SRCEC3; ISSN:2045-2322. (Nature Publishing Group)
      Materials scientists increasingly employ machine or statistical learning (SL) techniques to accelerate materials discovery and design. Such pursuits benefit from pooling training data across, and thus being able to generalize predictions over, k-nary compds. of diverse chemistries and structures. This work presents a SL framework that addresses challenges in materials science applications, where datasets are diverse but of modest size, and extreme values are often of interest. Our advances include the application of power or Holder means to construct descriptors that generalize over chem. and crystal structure, and the incorporation of multivariate local regression within a gradient boosting framework. The approach is demonstrated by developing SL models to predict bulk and shear moduli (K and G, resp.) for polycryst. inorg. compds., using 1,940 compds. from a growing database of calcd. elastic moduli for metals, semiconductors and insulators. The usefulness of the models is illustrated by screening for superhard materials.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xhs1amtL%252FO&md5=dbe3625fe35277822c6ae92c7dcbfee7
    50. 50
      Isayev, O.; Oses, C.; Toher, C.; Gossett, E.; Curtarolo, S.; Tropsha, A. Universal fragment descriptors for predicting properties of inorganic crystals. Nat. Commun. 2017, 8, 15679,  DOI: 10.1038/ncomms15679
      50
      Universal fragment descriptors for predicting properties of inorganic crystals
      Isayev, Olexandr; Oses, Corey; Toher, Cormac; Gossett, Eric; Curtarolo, Stefano; Tropsha, Alexander
      Nature Communications (2017), 8 (), 15679CODEN: NCAOBW; ISSN:2041-1723. (Nature Publishing Group)
      A review. Although historically materials discovery has been driven by a laborious trial-and-error process, knowledge-driven materials design can now be enabled by the rational combination of Machine Learning methods and materials databases. Here, data from the AFLOW repository for ab initio calcns. is combined with Quant. Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temp. and heat capacities. The prediction's accuracy compares well with the quality of the training data for virtually any stoichiometric inorg. cryst. material, reciprocating the available thermomech. exptl. data. The universality of the approach is attributed to the construction of the descriptors: Property-Labeled Materials Fragments. The representations require only minimal structural input allowing straightforward implementations of simple heuristic design rules.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXpt1Oksb8%253D&md5=543e2069263b9b1b1a121667866e2c5a
    51. 51
      Fujimura, K.; Seko, A.; Koyama, Y.; Kuwabara, A.; Kishida, I.; Shitara, K.; Fisher, C. A. J.; Moriwake, H.; Tanaka, I. Accelerated Materials Design of Lithium Superionic Conductors Based on First-Principles Calculations and Machine Learning Algorithms. Adv. Energy Mater. 2013, 3, 980– 985,  DOI: 10.1002/aenm.201300060
      51
      Accelerated materials design of lithium superionic conductors based on first-principles calculations and machine learning algorithms
      Fujimura, Koji; Seko, Atsuto; Koyama, Yukinori; Kuwabara, Akihide; Kishida, Ippei; Shitara, Kazuki; Fisher, Craig A. J.; Moriwake, Hiroki; Tanaka, Isao
      Advanced Energy Materials (2013), 3 (8), 980-985CODEN: ADEMBC; ISSN:1614-6840. (Wiley-Blackwell)
      In this article, results of systematic sets of first-principles calcns. based on the cluster expansion method, as well as first-principles mol. dynamics (FPMD) simulations carried out to calc. lithium-ion conductivities at high temp., for a diverse range of compns. is studied. A machine-learning technique is used to combine theor. and exptl. datasets to predict the cond. of each compn. at 373 K. The insights obtained show that an iterative combination of first-principles calcns. and focused expts. can greatly accelerate the materials design process by enabling a wide compositional and structural phase space to be examd. efficiently.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtlWhsbzM&md5=4ec409403b25e39e04cc760523694fa3
    52. 52
      Sendek, A. D.; Yang, Q.; Cubuk, E. D.; Duerloo, K.-A. N.; Cui, Y.; Reed, E. J. Holistic computational structure screening of more than 12000 candidates for solid lithium-ion conductor materials. Energy Environ. Sci. 2017, 10, 306– 320,  DOI: 10.1039/C6EE02697D
      52
      Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials
      Sendek, Austin D.; Yang, Qian; Cubuk, Ekin D.; Duerloo, Karel-Alexander N.; Cui, Yi; Reed, Evan J.
      Energy & Environmental Science (2017), 10 (1), 306-320CODEN: EESNBY; ISSN:1754-5706. (Royal Society of Chemistry)
      We present a new type of large-scale computational screening approach for identifying promising candidate materials for solid state electrolytes for lithium ion batteries that is capable of screening all known lithium contg. solids. To be useful for batteries, high performance solid state electrolyte materials must satisfy many requirements at once, an optimization that is difficult to perform exptl. or with computationally expensive ab initio techniques. We first screen 12 831 lithium contg. cryst. solids for those with high structural and chem. stability, low electronic cond., and low cost. We then develop a data-driven ionic cond. classification model using logistic regression for identifying which candidate structures are likely to exhibit fast lithium conduction based on exptl. measurements reported in the literature. The screening reduces the list of candidate materials from 12 831 down to 21 structures that show promise as electrolytes, few of which have been examd. exptl. We discover that none of our simple atomistic descriptor functions alone provide predictive power for ionic cond., but a multi-descriptor model can exhibit a useful degree of predictive power. We also find that screening for structural stability, chem. stability and low electronic cond. eliminates 92.2% of all Li-contg. materials and screening for high ionic cond. eliminates a further 93.3% of the remainder. Our screening utilizes structures and electronic information contained in the Materials Project database.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhvFymtbrP&md5=97cc808518346d0a599f623a2b35b7e4
    53. 53
      Evans, J. D.; Coudert, F.-X. Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning. Chem. Mater. 2017, 29, 7833– 7839,  DOI: 10.1021/acs.chemmater.7b02532
      53
      Predicting the Mechanical Properties of Zeolite Frameworks by Machine Learning
      Evans, Jack D.; Coudert, Francois-Xavier
      Chemistry of Materials (2017), 29 (18), 7833-7839CODEN: CMATEX; ISSN:0897-4756. (American Chemical Society)
      We show here that machine learning is a powerful new tool for predicting the elastic response of zeolites. We built our machine learning approach relying on geometric features only, which are related to local geometry, structure, and porosity of a zeolite, to predict bulk and shear moduli of zeolites with an accuracy exceeding that of force field approaches. The development of this model has illustrated clear correlations between characteristic features of a zeolite and elastic moduli, providing exceptional insight into the mechanics of zeolitic frameworks. Finally, we employ this methodol. to predict the elastic response of 590,448 hypothetical zeolites, and the results of this massive database provide clear evidence of stability trends in porous materials.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtl2lsLfK&md5=5745ca383f3204d7c606d9eec2953d22
    54. 54
      Ahmad, Z.; Viswanathan, V. Role of anisotropy in determining stability of electrodeposition at solid-solid interfaces. Phys. Rev. Materials 2017, 1, 055403,  DOI: 10.1103/PhysRevMaterials.1.055403
      There is no corresponding record for this reference.
    55. 55
      Xu, C.; Ahmad, Z.; Aryanfar, A.; Viswanathan, V.; Greer, J. R. Enhanced strength and temperature dependence of mechanical properties of Li at small scales and its implications for Li metal anodes. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 57– 61,  DOI: 10.1073/pnas.1615733114
      55
      Enhanced strength and temperature dependence of mechanical properties of Li at small scales and its implications for Li metal anodes
      Xu, Chen; Ahmad, Zeeshan; Aryanfar, Asghar; Viswanathan, Venkatasubramanian; Greer, Julia R.
      Proceedings of the National Academy of Sciences of the United States of America (2017), 114 (1), 57-61CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      Most next-generation Li ion battery chemistries require a functioning lithium metal (Li) anode. However, its application in secondary batteries has been inhibited because of uncontrollable dendrite growth during cycling. Mech. suppression of dendrite growth through solid polymer electrolytes (SPEs) or through robust separators has shown the most potential for alleviating this problem. Studies of the mech. behavior of Li at any length scale and temp. are limited because of its extreme reactivity, which renders sample prepn., transfer, microstructure characterization, and mech. testing extremely challenging. We conduct nanomech. expts. in an in situ scanning electron microscope and show that micrometer-sized Li attains extremely high strengths of 105 MPa at room temp. and of 35 MPa at 90 °C. We demonstrate that single-cryst. Li exhibits a power-law size effect at the micrometer and submicrometer length scales, with the strengthening exponent of -0.68 at room temp. and of -1.00 at 90 °C. We also report the elastic and shear moduli as a function of crystallog. orientation gleaned from expts. and first-principles calcns., which show a high level of anisotropy up to the m.p., where the elastic and shear moduli vary by a factor of ∼4 between the stiffest and most compliant orientations. The emergence of such high strengths in small-scale Li and sensitivity of this metal's stiffness to crystallog. orientation help explain why the existing methods of dendrite suppression have been mainly unsuccessful and have significant implications for practical design of future-generation batteries.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XitFWls7fM&md5=65404fc79b4b340710a0a4c287094cf0
    56. 56
      Shi, F.; Pei, A.; Vailionis, A.; Xie, J.; Liu, B.; Zhao, J.; Gong, Y.; Cui, Y. Strong texturing of lithium metal in batteries. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 12138– 12143,  DOI: 10.1073/pnas.1708224114
      56
      Strong texturing of lithium metal in batteries
      Shi, Feifei; Pei, Allen; Vailionis, Arturas; Xie, Jin; Liu, Bofei; Zhao, Jie; Gong, Yongji; Cui, Yi
      Proceedings of the National Academy of Sciences of the United States of America (2017), 114 (46), 12138-12143CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)
      Lithium, with its high theor. specific capacity and lowest electrochem. potential, was recognized as the ultimate neg. electrode material for next-generation lithium-based high-energy-d. batteries. However, a key challenge that has yet to be overcome is the inferior reversibility of Li plating and stripping, typically thought to be related to the uncontrollable morphol. evolution of the Li anode during cycling. Here we show that Li-metal texturing (preferential crystallog. orientation) occurs during electrochem. deposition, which governs the morphol. change of the Li anode. X-ray diffraction pole-figure anal. demonstrates that the texture of Li deposits is primarily dependent on the type of additive or cross-over mol. from the cathode side. With adsorbed additives, like LiNO3 and polysulfide, the lithium deposits are strongly textured, with Li (110) planes parallel to the substrate, and thus exhibit uniform, rounded morphol. A growth diagram of lithium deposits is given to connect various texture and morphol. scenarios for different battery electrolytes. This understanding of lithium electrocrystn. from the crystallog. point of view provides significant insight for future lithium anode materials design in high-energy-d. batteries.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhslemsrjO&md5=ca7238bf137ef10cd147f88226c28000
    57. 57
      Wang, Y.; Richards, W. D.; Ong, S. P.; Miara, L. J.; Kim, J. C.; Mo, Y.; Ceder, G. Design principles for solid-state lithium superionic conductors. Nat. Mater. 2015, 14, 1026– 1031,  DOI: 10.1038/nmat4369
      57
      Design principles for solid-state lithium superionic conductors
      Wang, Yan; Richards, William Davidson; Ong, Shyue Ping; Miara, Lincoln J.; Kim, Jae Chul; Mo, Yifei; Ceder, Gerbrand
      Nature Materials (2015), 14 (10), 1026-1031CODEN: NMAACR; ISSN:1476-1122. (Nature Publishing Group)
      Lithium solid electrolytes can potentially address two key limitations of the org. electrolytes used in today's lithium-ion batteries, namely, their flammability and limited electrochem. stability. However, achieving a Li+ cond. in the solid state comparable to existing liq. electrolytes (>1 mS cm-1) is particularly challenging. In this work, we reveal a fundamental relationship between anion packing and ionic transport in fast Li-conducting materials and expose the desirable structural attributes of good Li-ion conductors. We find that an underlying body-centered cubic-like anion framework, which allows direct Li hops between adjacent tetrahedral sites, is most desirable for achieving high ionic cond., and that indeed this anion arrangement is present in several known fast Li-conducting materials and other fast ion conductors. These findings provide important insight towards the understanding of ionic transport in Li-ion conductors and serve as design principles for future discovery and design of improved electrolytes for Li-ion batteries.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhtlCksb%252FI&md5=114ad3946493cf35ef3ee5d65e37c2d7
    58. 58
      Shannon, R. D. Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides. Acta Crystallogr., Sect. A: Cryst. Phys., Diffr., Theor. Gen. Crystallogr. 1976, 32, 751– 767,  DOI: 10.1107/S0567739476001551
      There is no corresponding record for this reference.
    59. 59
      Gotoh, K.; Finney, J. L. Statistical geometrical approach to random packing density of equal spheres. Nature 1974, 252, 202,  DOI: 10.1038/252202a0
      There is no corresponding record for this reference.
    60. 60
      Stepanyuk, V.; Szasz, A.; Katsnelson, A.; Trushin, O.; Müller, H.; Kirchmayr, H. Microstructure and its relaxation in FeB amorphous system simulated by moleculular dynamics. J. Non-Cryst. Solids 1993, 159, 80– 87,  DOI: 10.1016/0022-3093(93)91284-A
      There is no corresponding record for this reference.
    61. 61
      Ong, S. P.; Richards, W. D.; Jain, A.; Hautier, G.; Kocher, M.; Cholia, S.; Gunter, D.; Chevrier, V. L.; Persson, K. A.; Ceder, G. Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis. Comput. Mater. Sci. 2013, 68, 314– 319,  DOI: 10.1016/j.commatsci.2012.10.028
      61
      Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis
      Ong, Shyue Ping; Richards, William Davidson; Jain, Anubhav; Hautier, Geoffroy; Kocher, Michael; Cholia, Shreyas; Gunter, Dan; Chevrier, Vincent L.; Persson, Kristin A.; Ceder, Gerbrand
      Computational Materials Science (2013), 68 (), 314-319CODEN: CMMSEM; ISSN:0927-0256. (Elsevier B.V.)
      We present the Python Materials Genomics (pymatgen) library, a robust, open-source Python library for materials anal. A key enabler in high-throughput computational materials science efforts is a robust set of software tools to perform initial setup for the calcns. (e.g., generation of structures and necessary input files) and post-calcn. anal. to derive useful material properties from raw calcd. data. The pymatgen library aims to meet these needs by (1) defining core Python objects for materials data representation, (2) providing a well-tested set of structure and thermodn. analyses relevant to many applications, and (3) establishing an open platform for researchers to collaboratively develop sophisticated analyses of materials data obtained both from first principles calcns. and expts. The pymatgen library also provides convenient tools to obtain useful materials data via the Materials Project's REpresentational State Transfer (REST) Application Programming Interface (API). As an example, using pymatgen's interface to the Materials Project's RESTful API and phase diagram package, we demonstrate how the phase and electrochem. stability of a recently synthesized material, Li4SnS4, can be analyzed using a min. of computing resources. We find that Li4SnS4 is a stable phase in the Li-Sn-S phase diagram (consistent with the fact that it can be synthesized), but the narrow range of lithium chem. potentials for which it is predicted to be stable would suggest that it is not intrinsically stable against typical electrodes used in lithium-ion batteries.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhsVGjt7g%253D&md5=104f567dbd8f4199911ded91bc42100e
    62. 62
      Pannikkat, A.; Raj, R. Measurement of an electrical potential induced by normal stress applied to the interface of an ionic material at elevated temperatures. Acta Mater. 1999, 47, 3423– 3431,  DOI: 10.1016/S1359-6454(99)00206-2
      62
      Measurement of an electrical potential induced by normal stress applied to the interface of an ionic material at elevated temperatures
      Pannikkat, A. K.; Raj, R.
      Acta Materialia (1999), 47 (12), 3423-3431CODEN: ACMAFD; ISSN:1359-6454. (Elsevier Science Ltd.)
      The measurement of a p.d. between 2 surfaces of ZrO2 is reported, when a normal stress is applied to one surface, leaving the other surface stress free. The p.d. is proportional to the applied stress over a wide range. The proportionality const. represents a new thermodn. measurement of the interfacial state because the measurement is reversible and independent of temp. In ZrO2, the proportionality const. is related to the vol. and the charge on the O ion by considering thermodn. equil. among the electrochem. potentials of the O ion at the stressed and unstressed interfaces. The agreement with theory is within 10% for specimens made of polycryst. ZrO2, or single crystal cubic ZrO2 of (100) orientation. The proportionality const. changes by up to 20% for other orientations of the single crystal; this change is attributed to differences in the effective charge on the O ion on different surface orientations. The kinetics of the voltage response was also investigated in detail; it is consistent with the diffusion of the O ion along the interface formed between the metal electrode and the ZrO2 surface. The present measurements provide the first exptl. confirmation of the fundamental relationship between the chem. potential, the normal traction, and the at. vol. of species at interfaces of cryst. materials. The measurement has implications in further understanding of diffusional creep, creep cavitation, and sintering in ionic (or partially ionic) solids.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK1MXmvVaku78%253D&md5=929fe736a48797d25a19d934c8079ebf
    63. 63
      Xie, T.; Grossman, J. C. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. Phys. Rev. Lett. 2018, 120, 145301,  DOI: 10.1103/PhysRevLett.120.145301
      63
      Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
      Xie, Tian; Grossman, Jeffrey C.
      Physical Review Letters (2018), 120 (14), 145301CODEN: PRLTAO; ISSN:1079-7114. (American Physical Society)
      The use of machine learning methods for accelerating the design of cryst. materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chem. insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of cryst. materials. Our method provides a highly accurate prediction of d. functional theory calcd. properties for eight different properties of crystals with various structure types and compns. after being trained with 104 data points. Further, our framework is interpretable because one can ext. the contributions from local chem. environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXltFSnu7c%253D&md5=93beb5675af86cf95e07c82c136f3511
    64. 64
      Jain, A.; Ong, S. P.; Hautier, G.; Chen, W.; Richards, W. D.; Dacek, S.; Cholia, S.; Gunter, D.; Skinner, D.; Ceder, G.; Persson, K. A. The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater. 2013, 1, 011002,  DOI: 10.1063/1.4812323
      64
      Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
      Jain, Anubhav; Ong, Shyue Ping; Hautier, Geoffroy; Chen, Wei; Richards, William Davidson; Dacek, Stephen; Cholia, Shreyas; Gunter, Dan; Skinner, David; Ceder, Gerbrand; Persson, Kristin A.
      APL Materials (2013), 1 (1), 011002/1-011002/11CODEN: AMPADS; ISSN:2166-532X. (American Institute of Physics)
      Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorg. materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform rapid-prototyping'' of new materials in silico, and provide researchers with new avenues for cost-effective, data-driven materials design. (c) 2013 American Institute of Physics.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtlyktLjF&md5=88cb8642abed05e6b34a2191519b3ff3
    65. 65
      de Jong, M.; Chen, W.; Angsten, T.; Jain, A.; Notestine, R.; Gamst, A.; Sluiter, M.; Krishna Ande, C.; van der Zwaag, S.; Plata, J. J.; Toher, C.; Curtarolo, S.; Ceder, G.; Persson, K. A.; Asta, M. Charting the complete elastic properties of inorganic crystalline compounds. Sci. Data 2015, 2, 150009,  DOI: 10.1038/sdata.2015.9
      65
      Charting the complete elastic properties of inorganic crystalline compounds
      de Jong, Maarten; Chen, Wei; Angsten, Thomas; Jain, Anubhav; Notestine, Randy; Gamst, Anthony; Sluiter, Marcel; Krishna Ande, Chaitanya; van der Zwaag, Sybrand; Plata, Jose J.; Toher, Cormac; Curtarolo, Stefano; Ceder, Gerbrand; Persson, Kristin A.; Asta, Mark
      Scientific Data (2015), 2 (), 150009CODEN: SDCABS; ISSN:2052-4463. (Nature Publishing Group)
      The elastic const. tensor of an inorg. compd. provides a complete description of the response of the material to external stresses in the elastic limit. It thus provides fundamental insight into the nature of the bonding in the material, and it is known to correlate with many mech. properties. Despite the importance of the elastic const. tensor, it has been measured for a very small fraction of all known inorg. compds., a situation that limits the ability of materials scientists to develop new materials with targeted mech. responses. To address this deficiency, we present here the largest database of calcd. elastic properties for inorg. compds. to date. The database currently contains full elastic information for 1,181 inorg. compds., and this no. is growing steadily. The methods used to develop the database are described, as are results of tests that establish the accuracy of the data. In addn., we document the database format and describe the different ways it can be accessed and analyzed in efforts related to materials discovery and design.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXoslOku74%253D&md5=0479a0c29355429904fd5fe14ca08cd8
    66. 66
      Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple. Phys. Rev. Lett. 1996, 77, 3865,  DOI: 10.1103/PhysRevLett.77.3865
      66
      Generalized gradient approximation made simple
      Perdew, John P.; Burke, Kieron; Ernzerhof, Matthias
      Physical Review Letters (1996), 77 (18), 3865-3868CODEN: PRLTAO; ISSN:0031-9007. (American Physical Society)
      Generalized gradient approxns. (GGA's) for the exchange-correlation energy improve upon the local spin d. (LSD) description of atoms, mols., and solids. We present a simple derivation of a simple GGA, in which all parameters (other than those in LSD) are fundamental consts. Only general features of the detailed construction underlying the Perdew-Wang 1991 (PW91) GGA are invoked. Improvements over PW91 include an accurate description of the linear response of the uniform electron gas, correct behavior under uniform scaling, and a smoother potential.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK28XmsVCgsbs%253D&md5=55943538406ee74f93aabdf882cd4630
    67. 67
      Paszke, A.; Gross, S.; Chintala, S.; Chanan, G.; Yang, E.; DeVito, Z.; Lin, Z.; Desmaison, A.; Antiga, L.; Lerer, A. Automatic differentiation in PyTorch , NIPS-W, 2017.
      There is no corresponding record for this reference.
    68. 68
      Tikekar, M. D.; Archer, L. A.; Koch, D. L. Stabilizing electrodeposition in elastic solid electrolytes containing immobilized anions. Sci. Adv. 2016, 2, 1600320,  DOI: 10.1126/sciadv.1600320
      68
      Stabilizing electrodeposition in elastic solid electrolytes containing immobilized anions
      Tikekar, Mukul D.; Archer, Lynden A.; Koch, Donald L.
      Science Advances (2016), 2 (7), e1600320/1-e1600320/15CODEN: SACDAF; ISSN:2375-2548. (American Association for the Advancement of Science)
      Ion transport - driven instabilities in electrodeposition of metals that lead to morphol. instabilities and dendrites are receiving renewed attention because mitigation strategies are needed for improving recharge-ability and safety of lithium batteries. The growth rate of these morphol. instabilities can be slowed by immobilizing a fraction of anions within the electrolyte to reduce the elec. field at the metal electrode. We analyze the role of elastic deformation of the solid electrolyte with immobilized anions and present theory combining the roles of separator elasticity and modified transport to evaluate the factors affecting the stability of planar deposition over a wide range of current densities. We find that stable electrodeposition can be easily achieved even at relatively high current densities in electrolytes/separators with moderate polymer-like mech. moduli, provided a small fraction of anions are immobilized in the separator.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXkslKrtLo%253D&md5=63591374b78e8f147b116ede066d3fa7
    69. 69
      Seino, Y.; Ota, T.; Takada, K.; Hayashi, A.; Tatsumisago, M. A sulphide lithium super ion conductor is superior to liquid ion conductors for use in rechargeable batteries. Energy Environ. Sci. 2014, 7, 627– 631,  DOI: 10.1039/C3EE41655K
      69
      A sulphide lithium super ion conductor is superior to liquid ion conductors for use in rechargeable batteries
      Seino, Yoshikatsu; Ota, Tsuyoshi; Takada, Kazunori; Hayashi, Akitoshi; Tatsumisago, Masahiro
      Energy & Environmental Science (2014), 7 (2), 627-631CODEN: EESNBY; ISSN:1754-5706. (Royal Society of Chemistry)
      We report that a heat-treated Li2S-P2S5 glass-ceramic conductor has an extremely high ionic cond. of 1.7 × 10-2 S cm-1 and the lowest conduction activation energy of 17 kJ mol-1 at room temp. among lithium-ion conductors reported to date. The optimum conditions of the heat treatment reduce the grain boundary resistance, and the influence of voids, to increase the Li+ ionic cond. of the solid electrolyte so that it is greater than the conductivities of liq. electrolytes, when the transport no. of lithium ions in the inorg. electrolyte is unity.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXhsFakt7Y%253D&md5=bdd225e1bf4147f70baf646869b2c4f7
    70. 70
      Li, Y.; Li, Y.; Pei, A.; Yan, K.; Sun, Y.; Wu, C.-L.; Joubert, L.-M.; Chin, R.; Koh, A. L.; Yu, Y.; Perrino, J.; Butz, B.; Chu, S.; Cui, Y. Atomic structure of sensitive battery materials and interfaces revealed by cryo–electron microscopy. Science 2017, 358, 506– 510,  DOI: 10.1126/science.aam6014
      70
      Atomic structure of sensitive battery materials and interfaces revealed by cryo-electron microscopy
      Li, Yuzhang; Li, Yanbin; Pei, Allen; Yan, Kai; Sun, Yongming; Wu, Chun-Lan; Joubert, Lydia-Marie; Chin, Richard; Koh, Ai Leen; Yu, Yi; Perrino, John; Butz, Benjamin; Chu, Steven; Cui, Yi
      Science (Washington, DC, United States) (2017), 358 (6362), 506-510CODEN: SCIEAS; ISSN:0036-8075. (American Association for the Advancement of Science)
      Whereas std. transmission electron microscopy studies are unable to preserve the native state of chem. reactive and beam-sensitive battery materials after operation, such materials remain pristine at cryogenic conditions. It is then possible to atomically resolve individual Li metal atoms and their interface with the solid electrolyte interphase (SEI). We observe that dendrites in carbonate-based electrolytes grow along the <111> (preferred), <110>, or <211> directions as faceted, single-cryst. nanowires. These growth directions can change at kinks with no observable crystallog. defect. We reveal distinct SEI nanostructures formed in different electrolytes.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhslSgsb7O&md5=313b19162d2034ecf59cdef499b39565
    71. 71
      Stroh, A. N. Dislocations and Cracks in Anisotropic Elasticity. Philos. Mag. 1958, 3, 625– 646,  DOI: 10.1080/14786435808565804
      71
      Dislocations and cracks in anisotropic elasticity
      Stroh, A. N.
      Philosophical Magazine (1798-1977) (1958), 3 (), 625-46CODEN: PHMAA4; ISSN:0031-8086.
      The solution of the elastic equations is considered for the case in which the state of the solid is independent of 1 of the 3 Cartesian co.ovrddot.ordinates. The stresses due to a dislocation, a wall of parallel dislocations, and a crack in an arbitrary nonuniform stress field are obtained. The results hold for the most general anisotropy in which no symmetry elements of the crystal are assumed.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaG1MXitV2ltQ%253D%253D&md5=4195bf838814988673bec6632a6b1dc8
    72. 72
      Stroh, A. N. Steady State Problems in Anisotropic Elasticity. J. Math. Phys. 1962, 41, 77– 103,  DOI: 10.1002/sapm196241177
      There is no corresponding record for this reference.
    73. 73
      Hall, S. R.; Allen, F. H.; Brown, I. D. The crystallographic information file (CIF): a new standard archive file for crystallography. Acta Crystallogr., Sect. A: Found. Crystallogr. 1991, 47, 655– 685,  DOI: 10.1107/S010876739101067X
      There is no corresponding record for this reference.
    74. 74
      Mouhat, F.; Coudert, F. m. c.-X. Necessary and sufficient elastic stability conditions in various crystal systems. Phys. Rev. B: Condens. Matter Mater. Phys. 2014, 90, 224104,  DOI: 10.1103/PhysRevB.90.224104
      74
      Necessary and sufficient elastic stability conditions in various crystal systems
      Mouhat, Felix; Coudert, Francois-Xavier
      Physical Review B: Condensed Matter and Materials Physics (2014), 90 (22), 224104/1-224104/4, 4 pp.CODEN: PRBMDO; ISSN:1098-0121. (American Physical Society)
      While the Born elastic stability criteria are well known for cubic crystals, there is some confusion in the literature about the form they should take for lower-symmetry crystal classes. Here we present closed form necessary and sufficient conditions for elastic stability in all crystal classes, as a concise and pedagogical ref. to stability criteria in noncubic materials.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXivVGkur0%253D&md5=d8f5d43f831b6e45962e567bee94a008
    75. 75
      Ahmad, Z.; Viswanathan, V. Quantification of uncertainty in first-principles predicted mechanical properties of solids: Application to solid ion conductors. Phys. Rev. B: Condens. Matter Mater. Phys. 2016, 94, 064105,  DOI: 10.1103/PhysRevB.94.064105
      There is no corresponding record for this reference.
    76. 76
      Friedman, J. H. Greedy Function Approximation: A Gradient Boosting Machine. Ann. Stat. 2001, 29, 1189– 1232,  DOI: 10.1214/aos/1013203451
      There is no corresponding record for this reference.
    77. 77
      Friedman, J. H.; Hastie, T.; Tibshirani, R. The elements of statistical learning; Springer series in statistics: New York, 2001; Vol. 1.
      There is no corresponding record for this reference.
    78. 78
      Freund, Y.; Schapire, R. E. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. J. Comput. Syst. Sci. 1997, 55, 119– 139,  DOI: 10.1006/jcss.1997.1504
      There is no corresponding record for this reference.
    79. 79
      Drucker, H. Improving Regressors Using Boosting Techniques , Proceedings of the Fourteenth International Conference on Machine Learning, San Francisco, CA, United States, 1997; pp 107– 115.
      There is no corresponding record for this reference.
    80. 80
      Smola, A. J.; Schülkopf, B. A tutorial on support vector regression. Statistics and Computing 2004, 14, 199– 222,  DOI: 10.1023/B:STCO.0000035301.49549.88
      There is no corresponding record for this reference.
    81. 81
      MacKay, D. J. C. Bayesian Interpolation. Neural Comput. 1992, 4, 415– 447,  DOI: 10.1162/neco.1992.4.3.415
      There is no corresponding record for this reference.
    82. 82
      Pedregosa, F. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2011, 12, 2825– 2830
      There is no corresponding record for this reference.
    83. 83
      Deng, Z.; Wang, Z.; Chu, I.-H.; Luo, J.; Ong, S. P. Elastic Properties of Alkali Superionic Conductor Electrolytes from First Principles Calculations. J. Electrochem. Soc. 2016, 163, A67– A74,  DOI: 10.1149/2.0061602jes
      83
      Elastic Properties of Alkali Superionic Conductor Electrolytes from First Principles Calculations
      Deng, Zhi; Wang, Zhenbin; Chu, Iek-Heng; Luo, Jian; Ong, Shyue Ping
      Journal of the Electrochemical Society (2016), 163 (2), A67-A74CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
      In this work, we present a comprehensive investigation of the elastic properties (the full elastic tensor, bulk, shear and Young's moduli, and Poisson's ratio) of 23 well-known ceramic alkali superionic conductor electrolytes (SICEs) using first principles calcns. We find that the computed elastic moduli are in good agreement with exptl. data (wherever available) and chem. bonding nature. The anion species and structural framework have a significant influence on the elastic properties, and the relative elastic moduli of the various classes of SICEs follow the order thiophosphate < antiperovskite < phosphate < NASICON < garnet < perovskite. Within the same framework structure, we observe that Na SICEs are softer than their Li analogs. We discuss the implications of these findings in the context of fabrication, battery operation, and enabling a Li metal anode. The data computed in this work will also serve as a useful ref. for future expts. as well as theor. modeling of SICEs for rechargeable alkali-ion batteries.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhvFylurvI&md5=b8172c27880503a159ab9fe8a1a7c6c3
    84. 84
      Ranganathan, S. I.; Ostoja-Starzewski, M. Universal Elastic Anisotropy Index. Phys. Rev. Lett. 2008, 101, 055504,  DOI: 10.1103/PhysRevLett.101.055504
      84
      Universal elastic anisotropy index
      Ranganathan, Shivakumar I.; Ostoja-Starzewski, Martin
      Physical Review Letters (2008), 101 (5), 055504/1-055504/4CODEN: PRLTAO; ISSN:0031-9007. (American Physical Society)
      Practically all elastic single crystals are anisotropic, which calls for an appropriate universal measure to quantify the extent of anisotropy. A review of the existing anisotropy measures in the literature leads to a conclusion that they lack universality in the sense that they are nonunique and ignore contributions from the bulk part of the elastic stiffness (or compliance) tensor. Proceeding from extremal principles of elasticity, the authors introduce a new universal anisotropy index that overcomes the above limitations. Also, the authors establish special relations between the proposed anisotropy index and the existing anisotropy measures for special cases. A new elastic anisotropy diagram is constructed for over 100 different crystals (from cubic through triclinic), demonstrating that the proposed anisotropy measure is applicable to all types of elastic single crystals, and thus fills an important void in the existing literature.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1cXhtVSju7nN&md5=40a1e4eb5d42153f25575b60665ccab9
    85. 85
      Lu, Z.; Ciucci, F. Metal Borohydrides as Electrolytes for Solid-State Li, Na, Mg, and Ca Batteries: A First-Principles Study. Chem. Mater. 2017, 29, 9308– 9319,  DOI: 10.1021/acs.chemmater.7b03284
      85
      Metal Borohydrides as Electrolytes for Solid-State Li, Na, Mg, and Ca Batteries: A First-Principles Study
      Lu, Ziheng; Ciucci, Francesco
      Chemistry of Materials (2017), 29 (21), 9308-9319CODEN: CMATEX; ISSN:0897-4756. (American Chemical Society)
      Metal borohydrides are a family of materials that were recently discovered to have extraordinary ionic conductivities, making them promising candidates as electrolytes for solid-state batteries (SSBs). In fact, various groups have measured the ionic conductivities and assembled batteries using specific borohydrides. However, there are no comprehensive studies assessing the thermodn. properties or discussing the suitability of metal borohydrides as electrolytes in SSBs, esp. for beyond-lithium applications. In this work, we investigate the electrochem. stability, interfacial characteristics, mech. properties, and ionic conductivities of Li, Na, Ca, and Mg borohydrides using first-principles calcns. Our results suggest that Li and Na borohydrides are unstable at high voltages. However, the corresponding decompn. products, i.e., B12H122--contg. phases, have wide electrochem. windows which protect the electrolyte, leading to large electrochem. windows as wide as 5 V. In addn., our simulations indicate that metal borohydrides are ductile, suggesting facile processing. However, their low shear moduli may result in metal dendrite formation. For Ca and Mg borohydrides, while they possess reasonably good electrochem. stability, the low cationic diffusivity may impede their practical use. Finally, the anion rotation barrier was shown to correlate with the superionic phase transition temp., suggesting that anion mixing may be a potential approach to achieve room-temp. superionic cond.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhs1Sks77I&md5=4f24aceeeec626b708e8a2546488d8c9
    86. 86
      Varley, J. B.; Kweon, K.; Mehta, P.; Shea, P.; Heo, T. W.; Udovic, T. J.; Stavila, V.; Wood, B. C. Understanding Ionic Conductivity Trends in Polyborane Solid Electrolytes from Ab Initio Molecular Dynamics. ACS Energy Lett. 2017, 2, 250– 255,  DOI: 10.1021/acsenergylett.6b00620
      86
      Understanding Ionic Conductivity Trends in Polyborane Solid Electrolytes from Ab Initio Molecular Dynamics
      Varley, Joel B.; Kweon, Kyoung; Mehta, Prateek; Shea, Patrick; Heo, Tae Wook; Udovic, Terrence J.; Stavila, Vitalie; Wood, Brandon C.
      ACS Energy Letters (2017), 2 (1), 250-255CODEN: AELCCP; ISSN:2380-8195. (American Chemical Society)
      Polyborane salts based on B12H122-, B10H102-, CB11H12-, and CB9H10- demonstrate high Li and Na superionic cond. that makes them attractive as electrolytes in all-solid-state batteries. Their chem. and structural diversity creates a versatile design space that could be used to optimize materials with higher cond. at lower temps.; however, many mechanistic details remain enigmatic, including reasons why certain known modifications lead to improved performance. We use extensive ab initio mol. dynamics simulations to explore the dependence of ionic cond. on cation/anion pair combinations for Li and Na polyborane salts. Further simulations are used to probe the influence of local modifications to chem., stoichiometry, and compn. Carbon doping, anion alloying, and cation off-stoichiometry are found to favorably introduce intrinsic disorder, facilitating local deviation from the expected cation population. Lattice expansion likewise has a pos. effect by aiding anion reorientations that are crit. for conduction. Implications for engineering polyboranes for improved ionic cond. are discussed.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XitFektb3P&md5=10bc784c32c1869968e8c1ae8ab49401
    87. 87
      Tang, W. S.; Matsuo, M.; Wu, H.; Stavila, V.; Zhou, W.; Talin, A. A.; Soloninin, A. V.; Skoryunov, R. V.; Babanova, O. A.; Skripov, A. V.; Unemoto, A.; Orimo, S.; Udovic, T. J. Liquid-Like Ionic Conduction in Solid Lithium and Sodium Monocarba-closo-Decaborates Near or at Room Temperature. Adv. Energy Mater. 2016, 6, 1502237,  DOI: 10.1002/aenm.201502237
      There is no corresponding record for this reference.
    88. 88
      Tang, W. S.; Unemoto, A.; Zhou, W.; Stavila, V.; Matsuo, M.; Wu, H.; Orimo, S.-i.; Udovic, T. J. Unparalleled lithium and sodium superionic conduction in solid electrolytes with large monovalent cage-like anions. Energy Environ. Sci. 2015, 8, 3637– 3645,  DOI: 10.1039/C5EE02941D
      88
      Unparalleled lithium and sodium superionic conduction in solid electrolytes with large monovalent cage-like anions
      Tang, Wan Si; Unemoto, Atsushi; Zhou, Wei; Stavila, Vitalie; Matsuo, Motoaki; Wu, Hui; Orimo, Shin-ichi; Udovic, Terrence J.
      Energy & Environmental Science (2015), 8 (12), 3637-3645CODEN: EESNBY; ISSN:1754-5706. (Royal Society of Chemistry)
      Solid electrolytes with sufficiently high conductivities and stabilities are the elusive answer to the inherent shortcomings of org. liq. electrolytes prevalent in today's rechargeable batteries. We recently revealed a novel fast-ion-conducting sodium salt, Na2B12H12, which contains large, icosahedral, divalent B12H122- anions that enable impressive superionic cond., albeit only above its 529 K phase transition. Its lithium congener, Li2B12H12, possesses an even more technol. prohibitive transition temp. above 600 K. Here we show that the chem. related LiCB11H12 and NaCB11H12 salts, which contain icosahedral, monovalent CB11H12- anions, both exhibit much lower transition temps. near 400 K and 380 K, resp., and truly stellar ionic conductivities (>0.1 S cm-1) unmatched by any other known polycryst. materials at these temps. With proper modifications, we are confident that room-temp.-stabilized superionic salts incorporating such large polyhedral anion building blocks are attainable, thus enhancing their future prospects as practical electrolyte materials in next-generation, all-solid-state batteries.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2MXhs1amsr%252FL&md5=6ce33938ab3579da7ee531eb41f73964
    89. 89
      Malmgren, S.; Ciosek, K.; Lindblad, R.; Plogmaker, S.; Kühn, J.; Rensmo, H.; Edström, K.; Hahlin, M. Consequences of air exposure on the lithiated graphite SEI. Electrochim. Acta 2013, 105, 83– 91,  DOI: 10.1016/j.electacta.2013.04.118
      89
      Consequences of air exposure on the lithiated graphite SEI
      Malmgren, Sara; Ciosek, Katarzyna; Lindblad, Rebecka; Plogmaker, Stefan; Kuhn, Julius; Rensmo, Haakan; Edstroem, Kristina; Hahlin, Maria
      Electrochimica Acta (2013), 105 (), 83-91CODEN: ELCAAV; ISSN:0013-4686. (Elsevier Ltd.)
      Consequences of air exposure on the surface compn. of one of the most reactive Li-ion battery components, the lithiated graphite, was studied using 280-835 eV soft XPS (SOXPES) as well as 1486.7 eV XPS (∼2 and ∼10 nm probing depth, resp.). Different depth regions of the solid electrolyte interphase (SEI) of graphite cycled vs. LiFePO4 were thereby examd. Also, the air sensitivity of samples subject to four different combinations of pre-treatments (washed/unwashed and exposed to air before or after vacuum treatment) was explored. The samples showed important changes after exposure to air, which are largely dependent on sample pre-treatment. Changes after exposure of unwashed samples exposed before vacuum treatment were attributed to reactions involving volatile species. On washed, air exposed samples, as well as unwashed samples exposed after vacuum treatment, effects attributed to LiOH formation in the innermost SEI were obsd. and suggested to be assocd. with partial delithiation of the surface region of the lithiated graphite electrode. Also, effects that can be attributed to LiPF6 decompn. were obsd. However, these effects were less pronounced than those attributed to reactions involving solvent species and the lithiated graphite.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtVehsbvE&md5=6475c40a670e813d2a8de45c21003e64
    90. 90
      Tasaki, K.; Goldberg, A.; Lian, J.-J.; Walker, M.; Timmons, A.; Harris, S. Solubility of Lithium Salts Formed on the Lithium-Ion Battery Negative Electrode Surface in Organic Solvents. J. Electrochem. Soc. 2009, 156, A1019– A1027,  DOI: 10.1149/1.3239850
      90
      Solubility of Lithium Salts Formed on the Lithium-Ion Battery Negative Electrode Surface in Organic Solvents
      Tasaki, Ken; Goldberg, Alex; Lian, Jian-Jie; Walker, Merry; Timmons, Adam; Harris, Stephen J.
      Journal of the Electrochemical Society (2009), 156 (12), A1019-A1027CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
      The soly. of lithium salts in di-Me carbonate (DMC) found in solid electrolyte interface films was detd. The salt-DMC solns. were evapd., and the salts were transferred into water for ion cond. measurements. The salts examd. included Li2CO3, lithium oxalate [(LiCO2)2], LiF, LiOH, lithium Me carbonate (LiOCO2CH3), and lithium Et carbonate (LiOCO2C2H5). The salt molarity in DMC ranged from 9.6 × 10-4 mol/L (LiOCO2CH3) to 9 × 10-5 mol/L (Li2CO3) in the order of LiOCO2CH3 > LiOCO2C2H5 > LiOH > LiF > (LiCO2)2 > Li2CO3. XPS measurements on solid electrolyte interface films on the surface of the anode taken from a com. battery after soaking in DMC for 1 h suggested that the films can dissolve. Sep., the heat of dissoln. of the salts was calcd. from computer simulations for the same salts, including Li2O, lithium methoxide (LiOCH3), and dilithium ethylene glycol dicarbonate [(CH2OCO2Li)2:LiEDC] in both DMC and ethylene carbonate. The results from the computer simulations suggested that the order in which the salt was likely to dissolve in both DMC and ethylene carbonate was LiEDC > LiOCO2CH3 > LiOH > LiOCO2C2H5 > LiOCH3 > LiF > (LiCO2)2 > Li2CO3 > Li2O. This order agreed with the expt. in DMC within the exptl. error. Both expt. and computer simulations showed that the org. salts are more likely to dissolve in DMC than the inorg. salts. The calcns. also predicted that the salts dissolve more likely in ethylene carbonate than in DMC, in general. Moreover, the results from the study were used to discuss the capacity fading mechanism during the storage of lithium-ion batteries.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXhtlCmsbzN&md5=433ac8ee99379a475c1e174d35423704
    91. 91
      Matsuo, M.; Nakamori, Y.; ichi Orimo, S.; Maekawa, H.; Takamura, H. Lithium superionic conduction in lithium borohydride accompanied by structural transition. Appl. Phys. Lett. 2007, 91, 224103,  DOI: 10.1063/1.2817934
      91
      Lithium superionic conduction in lithium borohydride accompanied by structural transition
      Matsuo, Motoaki; Nakamori, Yuko; Orimo, Shin-ichi; Maekawa, Hideki; Takamura, Hitoshi
      Applied Physics Letters (2007), 91 (22), 224103/1-224103/3CODEN: APPLAB; ISSN:0003-6951. (American Institute of Physics)
      The elec. cond. of LiBH4 measured by a.c. complex impedance increased by 3 orders of magnitude due to structural transition from orthorhombic to hexagonal at ∼390 K. The hexagonal phase exhibited a high elec. cond. of about 10-3 S/cm. The cond. calcd. from the Nernst-Einstein equation using the correlation time obtained from 7Li NMR agreed with the measured elec. cond. The elec. cond. in the hexagonal phase is due to Li superionic conduction.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXhsVequ7zJ&md5=8949f5d9d2da1f61bf6ad230dac90749
    92. 92
      Johnson, R.; Biefeld, R.; Keck, J. Ionic conductivity in Li5AlO4 and LiOH. Mater. Res. Bull. 1977, 12, 577– 587,  DOI: 10.1016/0025-5408(77)90066-6
      92
      Ionic conductivity in lithium aluminum oxide (Li5AlO4) and lithium hydroxide
      Johnson, R. T., Jr.; Biefeld, R. M.; Keck, J. D.
      Materials Research Bulletin (1977), 12 (6), 577-87CODEN: MRBUAC; ISSN:0025-5408.
      The ionic cond. and thermal properties of Li5AlO4 and LiOH were measured in wet and dry environments. An endothehrmic reaction at ∼415°C and an assocd. large increase in cond. were obsd. both in Li5AlO4, in wet environment, and in LiOH. The large cond. increase in Li5AlO4 results from LiOH retained within the material. The reaction(s) for formation of LiOH within Li5AlO4 ad the assocd. elec. changes appear to be reversible as the environment switches from wet to dry at high temps. There is a significant (>1%) electronic contribution to the cond. in these materials.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaE2sXkslKqs7k%253D&md5=49b9b53cd2c56638c6e2539226cf0764
    93. 93
      Maekawa, H.; Matsuo, M.; Takamura, H.; Ando, M.; Noda, Y.; Karahashi, T.; Orimo, S.-i. Halide-Stabilized LiBH4, a Room-Temperature Lithium Fast-Ion Conductor. J. Am. Chem. Soc. 2009, 131, 894– 895,  DOI: 10.1021/ja807392k
      93
      Halide-Stabilized LiBH4, a Room-Temperature Lithium Fast-Ion Conductor
      Maekawa, Hideki; Matsuo, Motoaki; Takamura, Hitoshi; Ando, Mariko; Noda, Yasuto; Karahashi, Taiki; Orimo, Shin-ichi
      Journal of the American Chemical Society (2009), 131 (3), 894-895CODEN: JACSAT; ISSN:0002-7863. (American Chemical Society)
      A review on development of lithium superionic conductors based on LiBH4 and lithium halides. Using these compds., room-temp. high lithium ion cond. was imparted to a hydride system that had not been considered a lithium ion electrolyte. The electrochem. measurements showed a great advantage of this material as an extremely lightwt. lithium electrolyte for high energy d. batteries. Versatile properties of these materials make them suitable for use in all-solid-state batteries.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD1MXisValtQ%253D%253D&md5=7602a4b2a0f259ac649e6ce4dfe8e53b
    94. 94
      Das, S.; Ngene, P.; Norby, P.; Vegge, T.; de Jongh, P. E.; Blanchard, D. All-Solid-State Lithium-Sulfur Battery Based on a Nanoconfined LiBH4 Electrolyte. J. Electrochem. Soc. 2016, 163, A2029– A2034,  DOI: 10.1149/2.0771609jes
      94
      All-Solid-State Lithium-Sulfur Battery Based on a Nanoconfined LiBH4 Electrolyte
      Das, Supti; Ngene, Peter; Norby, Poul; Vegge, Tejs; de Jongh, Petra E.; Blanchard, Didier
      Journal of the Electrochemical Society (2016), 163 (9), A2029-A2034CODEN: JESOAN; ISSN:0013-4651. (Electrochemical Society)
      In this work we characterize all-solid-state lithium-sulfur batteries based on nano-confined LiBH4 in mesoporous silica as solid electrolytes. The nano-confined LiBH4 has fast ionic lithium cond. at room temp., 0.1 mScm-1, negligible electronic cond. and its cationic transport no. (t+ = 0.96), close to unity, demonstrates a purely cationic conductor. The electrolyte has an excellent stability against lithium metal. The behavior of the batteries is studied by cyclic voltammetry and repeated charge/discharge cycles in galvanostatic conditions. The batteries show good performance, delivering high capacities vs. sulfur mass, typically 1220 mAhg-1 after 40 cycles at moderate temp. (55°), 0.03 C rates and working voltage of 2 V.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28Xht1Krsr%252FP&md5=ee1106f5cdf799cd3505e1078501696f
    95. 95
      Morales-García, A.; Valero, R.; Illas, F. An Empirical, yet Practical Way To Predict the Band Gap in Solids by Using Density Functional Band Structure Calculations. J. Phys. Chem. C 2017, 121, 18862– 18866,  DOI: 10.1021/acs.jpcc.7b07421
      95
      An empirical, yet practical way to predict the band gap in solids by using density functional band structure calculations
      Morales-Garcia, Angel; Valero, Rosendo; Illas, Francesc
      Journal of Physical Chemistry C (2017), 121 (34), 18862-18866CODEN: JPCCCK; ISSN:1932-7447. (American Chemical Society)
      Band structure calcns. based on d. functional theory (DFT) with local or gradient-cor. exchange-correlation potentials are known to severely underestimate the band gap of semiconducting and insulating materials. Alternative approaches have been proposed: from semiempirical setups, such as the so-called DFT+U, to hybrid d. functionals using a fraction of nonlocal Fock exchange, to modifications of semilocal d. functionals. However, the resulting methods appear to be material dependent and lack theor. rigor. The rigorous many-body perturbation theory based on GW methods provides accurate results but at a very high computational cost. Hereby, we show that a linear correlation between the electronic band gaps obtained from std. DFT and GW approaches exists for most materials and argue that (1) this is a strong indication that the problem of predicting band gaps from std. DFT calcn. arises from the assignment of a phys. meaning to the Kohn-Sham energy levels rather than from intrinsic errors of the DFT methods and (2) it provides a practical way to obtain GW-like quality results from std. DFT calcns. The latter will be esp. useful for systems where the unit cell involves a large no. of atoms as in the case of doped or defect-contg. materials for which GW calcns. become unfeasible.
      >> More from SciFinder ®
      https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXht12mtLnE&md5=b0a676344af3832e6ff75461b9232229
    96. 96
      Towns, J.; Cockerill, T.; Dahan, M.; Foster, I.; Gaither, K.; Grimshaw, A.; Hazlewood, V.; Lathrop, S.; Lifka, D.; Peterson, G. D.; Roskies, R.; Scott, J. R.; Wilkins-Diehr, N. XSEDE: Accelerating Scientific Discovery. Comput. Sci. Eng. 2014, 16, 62– 74,  DOI: 10.1109/MCSE.2014.80
      There is no corresponding record for this reference.
    97. 97
      Nystrom, N. A.; Levine, M. J.; Roskies, R. Z.; Scott, J. R. Bridges: A Uniquely Flexible HPC Resource for New Communities and Data Analytics. Proceedings of the 2015 XSEDE Conference: Scientific Achievements Enabled by Enhanced Cyberinfrastructure 2015, 1– 8,  DOI: 10.1145/2792745.2792775
      There is no corresponding record for this reference.

  • Supporting Information

    Supporting Information

    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acscentsci.8b00229.

    • Details of machine-learning models and additional figures including schematics and visualizations, shear modulus values, and stability parameter values (PDF)

    • Anisotropic stability parameter for training data (XLSX)

    • Shear modulus of 60 648 compounds predicted using CGCNN (XLSX)

    • Anisotropic stability parameter for predicted data (XLSX)

    • Bulk modulus of 60 648 compounds predicted using CGCNN (XLSX)


    Terms & Conditions

    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.