Accurate Surface and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction PotentialsClick to copy article linkArticle link copied!
- Mgcini Keith PhuthiMgcini Keith PhuthiDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh 15213, Pennsylvania, United StatesMore by Mgcini Keith Phuthi
- Archie Mingze YaoArchie Mingze YaoDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh 15213, Pennsylvania, United StatesMore by Archie Mingze Yao
- Simon BatznerSimon BatznerSchool of Engineering and Applied Science, Harvard University, Cambridge 02138, Massachusetts, United StatesMore by Simon Batzner
- Albert MusaelianAlbert MusaelianSchool of Engineering and Applied Science, Harvard University, Cambridge 02138, Massachusetts, United StatesMore by Albert Musaelian
- Pinwen GuanPinwen GuanDepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh 15213, Pennsylvania, United StatesMore by Pinwen Guan
- Boris KozinskyBoris KozinskySchool of Engineering and Applied Science, Harvard University, Cambridge 02138, Massachusetts, United StatesMore by Boris Kozinsky
- Ekin Dogus Cubuk
- Venkatasubramanian Viswanathan*Venkatasubramanian Viswanathan*Email: [email protected]Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh 15213, Pennsylvania, United StatesMore by Venkatasubramanian Viswanathan
Abstract
The properties of lithium metal are key parameters in the design of lithium-ion and lithium-metal batteries. They are difficult to probe experimentally due to the high reactivity and low melting point of lithium as well as the microscopic scales at which lithium exists in batteries where it is found to have enhanced strength, with implications for dendrite suppression strategies. Computationally, there is a lack of empirical potentials that are consistently quantitatively accurate across all properties, and ab initio calculations are too costly. In this work, we train a machine learning interaction potential on density functional theory (DFT) data to state-of-the-art accuracy in reproducing experimental and ab initio results across a wide range of simulations at large length and time scales. We accurately predict thermodynamic properties, phonon spectra, temperature dependence of elastic constants, and various surface properties inaccessible using DFT. We establish that there exists a weak Bell–Evans–Polanyi relation correlating the self-adsorption energy and the minimum surface diffusion barrier for high Miller index facets.
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License Summary*
You are free to share(copy and redistribute) this article in any medium or format and to adapt(remix, transform, and build upon) the material for any purpose, even commercially within the parameters below:
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Attribution (BY): Credit must be given to the creator.
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1. Introduction
2. Methods
2.1. Machine Learning Interaction Potentials
2.2. DFT Data
Figure 1
Figure 1. Schematic showing the process by which data were generated and potentials were validated.
3. Results
3.1. Benchmarks against Density Functional Theory
property | DFT (other work) | DFT (this work) | DeepMD | NequIP32 | NequIP64 | SNAP | MEAM |
---|---|---|---|---|---|---|---|
energy RMSE (meV/atom) | 3 | 1 | 1 | ||||
force RMSE (meV/Å) | 20 | 12 | 12 | ||||
stress RMSE (GPa) | 0.22 | 0.06 | 0.06 | ||||
evaluation time (s) | 104 | 10–3 | 10–1 | 10–1 | 10–2 | 10–4 | |
lattice constant (Å) | 3.427 (16) | 3.434 | 3.434 [0.0] | 3.431 [−0.1] | 3.429 [−0.1] | 3.494 [1.7] | 3.506 [2.1] |
Ev (eV/atom) | 0.62 (16) | 0.525 | 0.518 [−1.4] | 0.567 [7.9] | 0.520 [−0.9] | 0.486 [−7.4] | 0.378 [−27.9] |
bulk modulus (GPa) | 14 (16) | 13.7 | 13.7 [0.0] | 14.0 [2.2] | 14.0 [2.3] | 10.5 [−23.7] | 12.9 [−5.7] |
C11 (GPa) | 15 (16) | 14.8 | 14.2 [−4.3] | 14.9 [0.4] | 14.7 [−0.4] | 18.4 [24.0] | 17.9 [21.2] |
C12 (GPa) | 13 (16) | 13.1 | 13.5 [2.4] | 13.6 [3.2] | 13.6 [3.8] | 6.5 [−50.5] | 10.4 [−20.9] |
C44 (GPa) | 11 (16) | 10.4 | 13.2 [26.5] | 10.9 [4.7] | 10.9 [4.7] | 10.0 [−3.7] | 12.7 [22.3] |
anisotropy | 11 (16) | 12.6 | 37.2 [196.1] | 16.8 [33.3] | 19.6 [56.1] | 1.7 [−86.5] | 3.4 [−73.1] |
(100) surf. energy (eV/Å2) | 0.029 (34) | 0.029 | 0.029 [0.5] | 0.029 [−0.8] | 0.029 [−0.7] | 0.027 [−7.1] | 0.024 [−15.9] |
(110) surf. energy (eV/Å2) | 0.031 (34) | 0.031 | 0.031 [0.0] | 0.031 [−0.6] | 0.031 [−0.1] | 0.028 [−9.4] | 0.024 [−21.9] |
(111) surf. energy (eV/Å2) | 0.034 (34) | 0.033 | 0.034 [3.0] | 0.033 [0.9] | 0.033 [0.2] | 0.030 [−8.6] | 0.028 [−14.6] |
HCP ΔE (meV/atom) | 0 (35) | 0 | –2 | 0 | 0 | –6 | 0 |
FCC ΔE (meV/atom) | 0 (35) | 0 | 0 | 0 | 0 | 0 | 0 |
BCC ΔE (meV/atom) | 2 (35) | 2 | 1 | 1 | 1 | –2 | 0 |
Percentage errors relative to the DFT prediction are shown in square brackets. Except for the anisotropy, all the errors are within 5% for the NequIP64 potential. The NequIP32 has a large vacancy formation energy error due to the lower precision used to calculate small energy differences. The SNAP and MEAM are likely to produce different results as they were trained on different data and parameterized differently than in this work.
3.2. Temperature Dependence of Elastic Properties of Lithium
Figure 2
Figure 2. Bulk mechanical properties of lithium as a function of temperature calculated using the NequIP32 and MEAM potential and compared to the experimental results (38−41) and the quasiharmonic approximation (QHA) (37) where possible. (a) Lattice constant as a function of temperature with error bars as standard deviations of the volume fluctuation in the NPT simulation. (b–d) C11, C12, and C44 elastic constants, respectively, with error bars as the standard error from the fitting of stress–strain curves. Note how the QHA fails to capture the behavior of C44. (e) Elastic anisotropy with error bars propagated from errors in the elastic constants. (f–i) Voigt–Reuss–Hill averaged bulk, shear, and Young’s moduli and the Poisson ratio, respectively.
3.3. Adsorption Energies and Surface Diffusion Barriers
Figure 3
Figure 3. Various surface properties calculated using the NequIP64 potential. (a) Demonstration of a BEP correlation between the adsorption energy and the minimum diffusion barrier of each facet. The (111) surface was not included in the fitting of the dotted line. (b) Calculation of the Ehrlich–Schwöebel barrier matching results in the literature. (c) A variety of SPESs colored by the relative adsorption energy show the different geometries of adsorption sites and the minimum diffusion barrier paths adatoms can take when diffusing from one surface unit cell to the next.
4. Discussion: Implications for LMB Design
Data Availability
The potentials and supporting data set used in this work are made available on Zenodo (2024). doi: 10.5281/zenodo.10470793.
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.3c10014.
Sata set, model parameters and simulation methods, data set distribution, additional benchmarks, elastic constant data for other potentials, and more facets (PDF)
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.
Acknowledgments
V.V. and M.K.P. acknowledge support from Google Collabs. This material is based on work that is partially funded by Google. A.M. was supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Department of Energy Computational Science Graduate Fellowship under award number DE-SC0021110. M.K.P., A.M.Y., and V.V. also acknowledge the Extreme Science and Engineering Discovery Environment (XSEDE) for providing computational resources through award no. TG-CTS180061.
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- 14Freitas, R.; Cao, Y. Machine-learning potentials for crystal defects. MRS Commun. 2022, 12, 510– 520, DOI: 10.1557/s43579-022-00221-5Google Scholar14https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XitFartLfO&md5=91109e3214f37917c8a991af24261678Machine-learning potentials for crystal defectsFreitas, Rodrigo; Cao, YifanMRS Communications (2022), 12 (5), 510-520CODEN: MCROF8; ISSN:2159-6867. (Springer International Publishing AG)Abstr.: Decades of advancements in strategies for the calcn. of at. interactions have culminated in a class of methods known as machine-learning interat. potentials (MLIAPs). MLIAPs dramatically widen the spectrum of materials systems that can be simulated with high phys. fidelity, including their microstructural evolution and kinetics. This framework, in conjunction with cross-scale simulations and in silico microscopy, is poised to bring a paradigm shift to the field of atomistic simulations of materials. In this prospective article we summarize recent progress in the application of MLIAPs to crystal defects. Graphical abstr.: [graphic not available: see fulltext].
- 15Owen, C. J.; Torrisi, S. B.; Xie, Y.; Batzner, S.; Coulter, J.; Musaelian, A.; Sun, L.; Kozinsky, B. Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set. arXiv 2023, arXiv:2302.12993, DOI: 10.48550/arXiv.2302.12993Google ScholarThere is no corresponding record for this reference.
- 16Zuo, Y.; Chen, C.; Li, X.; Deng, Z.; Chen, Y.; Behler, J.; Csányi, G.; Shapeev, A. V.; Thompson, A. P.; Wood, M. A.; Ong, S. P. Performance and cost assessment of machine learning interatomic potentials. J. Phys. Chem. A 2020, 124, 731– 745, DOI: 10.1021/acs.jpca.9b08723Google Scholar16https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmtVKjsg%253D%253D&md5=7716fe55d3269109bfc101fdfc25d823Performance and Cost Assessment of Machine Learning Interatomic PotentialsZuo, Yunxing; Chen, Chi; Li, Xiangguo; Deng, Zhi; Chen, Yiming; Behler, Jorg; Csanyi, Gabor; Shapeev, Alexander V.; Thompson, Aidan P.; Wood, Mitchell A.; Ong, Shyue PingJournal of Physical Chemistry A (2020), 124 (4), 731-745CODEN: JPCAFH; ISSN:1089-5639. (American Chemical Society)Machine learning of the quant. relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interat. potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs (ML-IAPs) based on four local environment descriptors-atom-centered symmetry functions (ACSF), smooth overlap of at. positions (SOAP), the spectral neighbor anal. potential (SNAP) bispectrum components, and moment tensors-using a diverse data set generated using high-throughput d. functional theory (DFT) calcns. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic consts. and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model and, consequently, computational cost. We will discuss these trade-offs in the context of model selection for mol. dynamics and other applications.
- 17Zhang, L.; Han, J.; Wang, H.; Car, R.; Weinan, E. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett. 2018, 120, 143001, DOI: 10.1103/PhysRevLett.120.143001Google Scholar17https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXltFSksrg%253D&md5=8b3f87603d7e1ea708341862744576e1Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum MechanicsZhang, Linfeng; Han, Jiequn; Wang, Han; Car, Roberto; E, WeinanPhysical Review Letters (2018), 120 (14), 143001CODEN: PRLTAO; ISSN:1079-7114. (American Physical Society)We introduce a scheme for mol. simulations, the deep potential mol. dynamics (DPMD) method, based on a many-body potential and interat. forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and mols. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.
- 18Jiao, J.; Lai, G.; Zhao, L.; Lu, J.; Li, Q.; Xu, X.; Jiang, Y.; He, Y.-B.; Ouyang, C.; Pan, F.; Li, H.; Zheng, J. Self-healing mechanism of lithium in lithium metal. Adv. Sci. 2022, 9, 2105574, DOI: 10.1002/advs.202105574Google Scholar18https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhtVGisLvJ&md5=d4890c132daf3a04d29b86499a13b782Self-Healing Mechanism of Lithium in Lithium MetalJiao, Junyu; Lai, Genming; Zhao, Liang; Lu, Jiaze; Li, Qidong; Xu, Xianqi; Jiang, Yao; He, Yan-Bing; Ouyang, Chuying; Pan, Feng; Li, Hong; Zheng, JiaxinAdvanced Science (Weinheim, Germany) (2022), 9 (12), 2105574CODEN: ASDCCF; ISSN:2198-3844. (Wiley-VCH Verlag GmbH & Co. KGaA)Li is an ideal anode material for use in state-of-the-art secondary batteries. However, Li-dendrite growth is a safety concern and results in low coulombic efficiency, which significantly restricts the com. application of Li secondary batteries. Unfortunately, the Li-deposition (growth) mechanism is poorly understood on the at. scale. Here, machine learning is used to construct a Li potential model with quantum-mech. computational accuracy. Mol. dynamics simulations in this study with this model reveal two self-healing mechanisms in a large Li-metal system, viz. surface self-healing, and bulk self-healing. It is concluded that self-healing occurs rapidly in nanoscale; thus, minimizing the voids between the Li grains using several comprehensive methods can effectively facilitate the formation of dendrite-free Li.
- 19Musaelian, A.; Batzner, S.; Johansson, A.; Sun, L.; Owen, C. J.; Kornbluth, M.; Kozinsky, B. Learning local equivariant representations for large-scale atomistic dynamics. arXiv 2022, arXiv:2204.05249Google ScholarThere is no corresponding record for this reference.
- 20Batatia, I.; Batzner, S.; Kovács, D. P.; Musaelian, A.; Simm, G. N. C.; Drautz, R.; Ortner, C.; Kozinsky, B.; Csányi, G. The design space of E(3)-equivariant atom-centered interatomic potentials. arXiv 2022, arXiv:2205.06643, DOI: 10.48550/arXiv.2205.06643Google ScholarThere is no corresponding record for this reference.
- 21Behler, J.; Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 2007, 98, 146401, DOI: 10.1103/PhysRevLett.98.146401Google Scholar21https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXjvF2ls7w%253D&md5=579a6cbf503565205acbb86ade0ae86bGeneralized Neural-Network Representation of High-Dimensional Potential-Energy SurfacesBehler, Jorg; Parrinello, MichelePhysical Review Letters (2007), 98 (14), 146401/1-146401/4CODEN: PRLTAO; ISSN:0031-9007. (American Physical Society)The accurate description of chem. processes often requires the use of computationally demanding methods like d.-functional theory (DFT), making long simulations of large systems unfeasible. In this Letter we introduce a new kind of neural-network representation of DFT potential-energy surfaces, which provides the energy and forces as a function of all at. positions in systems of arbitrary size and is several orders of magnitude faster than DFT. The high accuracy of the method is demonstrated for bulk silicon and compared with empirical potentials and DFT. The method is general and can be applied to all types of periodic and nonperiodic systems.
- 22Cubuk, E. D.; Malone, B. D.; Onat, B.; Waterland, A.; Kaxiras, E. Representations in neural network based empirical potentials. J. Chem. Phys. 2017, 147, 024104, DOI: 10.1063/1.4990503Google Scholar22https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtFKrsrrM&md5=ac6353696a39a0dc0251f6c08c3134f0Representations in neural network based empirical potentialsCubuk, Ekin D.; Malone, Brad D.; Onat, Berk; Waterland, Amos; Kaxiras, EfthimiosJournal of Chemical Physics (2017), 147 (2), 024104/1-024104/5CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)Many structural and mech. properties of crystals, glasses, and biol. macromols. can be modeled from the local interactions between atoms. These interactions ultimately derive from the quantum nature of electrons, which can be prohibitively expensive to simulate. Machine learning has the potential to revolutionize materials modeling due to its ability to efficiently approx. complex functions. For example, neural networks can be trained to reproduce results of d. functional theory calcns. at a much lower cost. However, how neural networks reach their predictions is not well understood, which has led to them being used as a "black box" tool. This lack of understanding is not desirable esp. for applications of neural networks in scientific inquiry. We argue that machine learning models trained on phys. systems can be used as more than just approxns. since they had to "learn" phys. concepts in order to reproduce the labels they were trained on. We use dimensionality redn. techniques to study in detail the representation of silicon atoms at different stages in a neural network, which provides insight into how a neural network learns to model at. interactions. (c) 2017 American Institute of Physics.
- 23Giannozzi, P.; Baroni, S.; Bonini, N.; Calandra, M.; Car, R.; Cavazzoni, C.; Ceresoli, D.; Chiarotti, G. L.; Cococcioni, M.; Dabo, I. QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials. J. Phys.: Condens. Matter 2009, 21, 395502, DOI: 10.1088/0953-8984/21/39/395502Google Scholar23https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3Mjltl2lug%253D%253D&md5=da053fa748721b6b381051a20e7a7f53QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materialsGiannozzi Paolo; Baroni Stefano; Bonini Nicola; Calandra Matteo; Car Roberto; Cavazzoni Carlo; Ceresoli Davide; Chiarotti Guido L; Cococcioni Matteo; Dabo Ismaila; Dal Corso Andrea; de Gironcoli Stefano; Fabris Stefano; Fratesi Guido; Gebauer Ralph; Gerstmann Uwe; Gougoussis Christos; Kokalj Anton; Lazzeri Michele; Martin-Samos Layla; Marzari Nicola; Mauri Francesco; Mazzarello Riccardo; Paolini Stefano; Pasquarello Alfredo; Paulatto Lorenzo; Sbraccia Carlo; Scandolo Sandro; Sclauzero Gabriele; Seitsonen Ari P; Smogunov Alexander; Umari Paolo; Wentzcovitch Renata MJournal of physics. Condensed matter : an Institute of Physics journal (2009), 21 (39), 395502 ISSN:.QUANTUM ESPRESSO is an integrated suite of computer codes for electronic-structure calculations and materials modeling, based on density-functional theory, plane waves, and pseudopotentials (norm-conserving, ultrasoft, and projector-augmented wave). The acronym ESPRESSO stands for opEn Source Package for Research in Electronic Structure, Simulation, and Optimization. It is freely available to researchers around the world under the terms of the GNU General Public License. QUANTUM ESPRESSO builds upon newly-restructured electronic-structure codes that have been developed and tested by some of the original authors of novel electronic-structure algorithms and applied in the last twenty years by some of the leading materials modeling groups worldwide. Innovation and efficiency are still its main focus, with special attention paid to massively parallel architectures, and a great effort being devoted to user friendliness. QUANTUM ESPRESSO is evolving towards a distribution of independent and interoperable codes in the spirit of an open-source project, where researchers active in the field of electronic-structure calculations are encouraged to participate in the project by contributing their own codes or by implementing their own ideas into existing codes.
- 24Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple [Phys. Rev. Lett. 77, 3865 (1996)]. Phys. Rev. Lett. 1997, 78, 1396, DOI: 10.1103/PhysRevLett.78.1396Google Scholar24https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2sXht1Gns7o%253D&md5=ecdb6e129b112a3a10e08cba26a083aeGeneralized gradient approximation made simple. [Erratum to document cited in CA126:51093]Perdew, John P.; Burke, Kieron; Ernzerhof, MatthiasPhysical Review Letters (1997), 78 (7), 1396CODEN: PRLTAO; ISSN:0031-9007. (American Physical Society)The errors were not reflected in the abstr. or the index entries.
- 25Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 1994, 50, 17953– 17979, DOI: 10.1103/PhysRevB.50.17953Google Scholar25https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2sfjslSntA%253D%253D&md5=1853d67af808af2edab58beaab5d3051Projector augmented-wave methodBlochlPhysical review. B, Condensed matter (1994), 50 (24), 17953-17979 ISSN:0163-1829.There is no expanded citation for this reference.
- 26Monkhorst, H. J.; Pack, J. D. Special points for Brillouin-zone integrations. Phys. Rev. B 1976, 13, 5188– 5192, DOI: 10.1103/PhysRevB.13.5188Google ScholarThere is no corresponding record for this reference.
- 27Methfessel, M.; Paxton, A. T. High-precision sampling for Brillouin-zone integration in metals. Phys. Rev. B 1989, 40, 3616– 3621, DOI: 10.1103/PhysRevB.40.3616Google Scholar27https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL1MXls1Slsr0%253D&md5=f10d684acee27eebaad6f576283d0310High-precision sampling for Brillouin-zone integration in metalsMethfessel, M.; Paxton, A. T.Physical Review B: Condensed Matter and Materials Physics (1989), 40 (6), 3616-21CODEN: PRBMDO; ISSN:0163-1829.A sampling method is given for Brillouin-zone integration in metals which converges exponentially with the no. of sampling points, without the loss of precision of normal broadening techniques. The scheme is based on smooth approximants to the δ and step functions which are constructed to give the exact result when integrating polynomials of a prescribed degree. In applications to the simple-cubic tight-binding band as well as to band structures of simple and transition metals, significant improvement over existing methods was shown. The method promises general applicability in the fields of total-energy calcns. and many-body physics.
- 28Zhang, Y.; Wang, H.; Chen, W.; Zeng, J.; Zhang, L.; Wang, H.; Weinan, E. DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models. Comput. Phys. Commun. 2020, 253, 107206, DOI: 10.1016/j.cpc.2020.107206Google Scholar28https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXjvFWjsrk%253D&md5=b803015c29378bc1d4bd8a2b4631c4ebDP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy modelsZhang, Yuzhi; Wang, Haidi; Chen, Weijie; Zeng, Jinzhe; Zhang, Linfeng; Wang, Han; E, WeinanComputer Physics Communications (2020), 253 (), 107206CODEN: CPHCBZ; ISSN:0010-4655. (Elsevier B.V.)In recent years, promising deep learning based interat. potential energy surface (PES) models have been proposed that can potentially allow us to perform mol. dynamics simulations for large scale systems with quantum accuracy. However, making these models truly reliable and practically useful is still a very non-trivial task. A key component in this task is the generation of datasets used in model training. In this paper, we introduce the Deep Potential GENerator (DP-GEN), an open-source software platform that implements the recently proposed "on-the-fly" learning procedure (Zhang et al. 2019) and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training. DP-GEN automatically and iteratively performs three steps: exploration, labeling, and training. It supports various popular packages for these three steps: LAMMPS for exploration, Quantum Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for training. It also allows automatic job submission and result collection on different types of machines, such as high performance clusters and cloud machines, and is adaptive to different job management tools, including Slurm, PBS, and LSF. As a concrete example, we illustrate the details of the process for generating a general-purpose PES model for Cu using DP-GEN. Program Title: DP-GENProgram Files doi:http://dx.doi.org/10.17632/sxybkgc5xc.1Licensing provisions: LGPLProgramming language: PythonNature of problem: Generating reliable deep learning based potential energy models with minimal human intervention and computational cost. Soln. method: The concurrent learning scheme is implemented. Supports for sampling configuration space with LAMMPS, generating ab initio data with Quantum Espresso, VASP, CP2K and training potential models with DeePMD-kit are provided. Supports for different machines including workstations, high performance clusters and cloud machines are provided. Supports for job management tools including Slurm, PBS, LSF are provided.
- 29Shinoda, W.; Shiga, M.; Mikami, M. Rapid estimation of elastic constants by molecular dynamics simulation under constant stress. Phys. Rev. B 2004, 69, 134103, DOI: 10.1103/PhysRevB.69.134103Google Scholar29https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXjvVWhtbY%253D&md5=de5cee06e65c288c55d2b57b9f3a62c2Rapid estimation of elastic constants by molecular dynamics simulation under constant stressShinoda, Wataru; Shiga, Motoyuki; Mikami, MasuhiroPhysical Review B: Condensed Matter and Materials Physics (2004), 69 (13), 134103/1-134103/8CODEN: PRBMDO; ISSN:0163-1829. (American Physical Society)Mol. simulations, when they are used to understand properties characterizing the mech. strength of solid materials, such as stress-strain relation or Born stability criterion, by using elastic consts., are sometimes seriously time consuming. In order to resolve this problem, we propose an efficient simulation approach under const. external stress and temp., modifying Parrinello-Rahman (PR) method using useful sampling techniques developed recently-massive Nose-Hoover chain method and hybrid Monte Carlo method. Test calcns. on the Ni crystal employing the embedded atom method have shown that our method greatly improved the efficiency in sampling the elastic properties compared with the conventional PR method.
- 30Kim, Y.-M.; Jung, I.-H.; Lee, B.-J. Atomistic modeling of pure Li and Mg–Li system. Modell. Simul. Mater. Sci. Eng. 2012, 20, 035005, DOI: 10.1088/0965-0393/20/3/035005Google Scholar30https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XmtVGqtr8%253D&md5=7e72312166b94c9dcd0feabea079c917Atomistic modeling of pure Li and Mg-Li systemKim, Young-Min; Jung, In-Ho; Lee, Byeong-JooModelling and Simulation in Materials Science and Engineering (2012), 20 (3), 035005/1-035005/13CODEN: MSMEEU; ISSN:1361-651X. (Institute of Physics Publishing)Interat. potentials for pure Li and the Mg-Li binary system have been developed based on the second nearest-neighbor modified embedded-atom method formalism. The potentials can describe various fundamental phys. properties of pure Li (bulk, point defect, planar defect and thermal properties) and alloy behaviors (thermodn., structural and elastic properties) in reasonable agreement with exptl. data or higher-level calcns. The applicability of the potential to atomistic investigations on the deformation behavior of Mg alloys and the effect of Li is demonstrated.
- 31Luo, S.; Zhang, Y.; Liu, X.; Wang, Z.; Fan, A.; Wang, H.; Ma, W.; Zhu, L.; Zhang, X. Thermal behavior of Li electrode in all-solid-state batteries and improved performance by temperature modulation. Int. J. Heat Mass Transfer 2022, 199, 123450, DOI: 10.1016/j.ijheatmasstransfer.2022.123450Google Scholar31https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XisF2iur7L&md5=a050d54d301a023ddc76fda546ca4c43Thermal behavior of Li electrode in all-solid-state batteries and improved performance by temperature modulationLuo, Shuting; Zhang, Yufeng; Liu, Xinyu; Wang, Zhenyu; Fan, Aoran; Wang, Haidong; Ma, Weigang; Zhu, Lingyun; Zhang, XingInternational Journal of Heat and Mass Transfer (2022), 199 (), 123450CODEN: IJHMAK; ISSN:0017-9310. (Elsevier Ltd.)All-solid-state lithium metal batteries (ASSLMBs) hold tremendous promise in elec. vehicles and portable electronic devices owing to the remarkably improved safety and energy d. However, the uncontrollable Li dendrites seriously hinder the cycling performance of ASSLMBs, and the fickle environment urgently requires a reliable energy supply over a wide temp. range. Understanding the temp.-dependent behavior of Li metal electrode holds great significance for breaking the bottleneck. Herein, we exptl. and theor. investigate the temp.-dependent morphol. evolution of electrodeposited Li in solid electrolyte from -20°C to 90°C. It is found that as temp. increases, nuclei size, nuclei d., structural compactness and growth direction of electrodeposited Li are significantly altered. The thermal effect on Li nucleation and growth is further revealed by mol. dynamics simulations. Based on this, we propose a temp. modulation strategy to improve the cyclability of Li metal electrode. By charging at the optimal temp., the cycling life and Coulomb efficiency of Li electrode can be simultaneously improved. Our investigation gives valuable insights towards temp.-dependent Li deposition in solid electrolyte and will provide rational guidance for the thermal management of ASSLMBs operated in extreme environments.
- 32Wang, X.; Pawar, G.; Li, Y.; Ren, X.; Zhang, M.; Lu, B.; Banerjee, A.; Liu, P.; Dufek, E. J.; Zhang, J.-G.; Xiao, J.; Liu, J.; Meng, Y. S.; Liaw, B. Glassy Li metal anode for high-performance rechargeable Li batteries. Nat. Mater. 2020, 19, 1339– 1345, DOI: 10.1038/s41563-020-0729-1Google Scholar32https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhsVGqtLjP&md5=86b6b978859de0b331c1073ca390c13bGlassy Li metal anode for high-performance rechargeable Li batteriesWang, Xuefeng; Pawar, Gorakh; Li, Yejing; Ren, Xiaodi; Zhang, Minghao; Lu, Bingyu; Banerjee, Abhik; Liu, Ping; Dufek, Eric J.; Zhang, Ji-Guang; Xiao, Jie; Liu, Jun; Meng, Ying Shirley; Liaw, BoryannNature Materials (2020), 19 (12), 1339-1345CODEN: NMAACR; ISSN:1476-1122. (Nature Research)Lithium metal has been considered an ideal anode for high-energy rechargeable Li batteries, although its nucleation and growth process remains mysterious, esp. at the nanoscale. Here, cryogenic transmission electron microscopy was used to reveal the evolving nanostructure of Li metal deposits at various transient states in the nucleation and growth process, in which a disorder-order phase transition was obsd. as a function of c.d. and deposition time. The at. interaction over wide spatial and temporal scales was depicted by reactive mol. dynamics simulations to assist in understanding the kinetics. Compared to cryst. Li, glassy Li outperforms in electrochem. reversibility, and it has a desired structure for high-energy rechargeable Li batteries. Our findings correlate the crystallinity of the nuclei with the subsequent growth of the nanostructure and morphol., and provide strategies to control and shape the mesostructure of Li metal to achieve high performance in rechargeable Li batteries.
- 33Ostadhossein, A.; Cubuk, E. D.; Tritsaris, G. A.; Kaxiras, E.; Zhang, S.; van Duin, A. C. T. Stress effects on the initial lithiation of crystalline silicon nanowires: reactive molecular dynamics simulations using ReaxFF. Phys. Chem. Chem. Phys. 2015, 17, 3832– 3840, DOI: 10.1039/c4cp05198jGoogle Scholar33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXitFegs7fL&md5=68fbf83ba3e8055458b6cbf9b232e765Stress effects on the initial lithiation of crystalline silicon nanowires: reactive molecular dynamics simulations using ReaxFFOstadhossein, Alireza; Cubuk, Ekin D.; Tritsaris, Georgios A.; Kaxiras, Efthimios; Zhang, Sulin; van Duin, Adri C. T.Physical Chemistry Chemical Physics (2015), 17 (5), 3832-3840CODEN: PPCPFQ; ISSN:1463-9076. (Royal Society of Chemistry)Silicon (Si) has been recognized as a promising anode material for the next-generation high-capacity lithium (Li)-ion batteries because of its high theor. energy d. Recent in situ transmission electron microscopy (TEM) revealed that the electrochem. lithiation of cryst. Si nanowires (c-SiNWs) proceeds by the migration of the interface between the lithiated Si (LixSi) shell and the pristine unlithiated core, accompanied by solid-state amorphization. The underlying at. mechanisms of Li insertion into c-Si remain poorly understood. Herein, we perform mol. dynamics (MD) simulations using the reactive force field (ReaxFF) to characterize the lithiation process of c-SiNWs. Our calcns. show that ReaxFF can accurately reproduce the energy barriers of Li migration from DFT calcns. in both cryst. (c-Si) and amorphous Si (a-Si). The ReaxFF-based MD simulations reveal that Li insertion into interlayer spacing between two adjacent (111) planes results in the peeling-off of the (111) facets and subsequent amorphization, in agreement with exptl. observations. We find that breaking of the Si-Si bonds between (111)-bilayers requires a rather high local Li concn., which explains the atomically sharp amorphous-cryst. interface (ACI). Our stress anal. shows that lithiation induces compressive stress at the ACI layer, causing retardation or even the stagnation of the reaction front, also in good agreement with TEM observations. Lithiation at high temps. (e.g. 1200 K) shows that Li insertion into c-SiNW results in an amorphous to cryst. phase transformation at Li:Si compn. of ∼4.2 : 1. Our modeling results provide a comprehensive picture of the effects of reaction and diffusion-induced stress on the interfacial dynamics and mech. degrdn. of SiNW anodes under chemo-mech. lithiation.
- 34Tran, R.; Xu, Z.; Radhakrishnan, B.; Winston, D.; Sun, W.; Persson, K. A.; Ong, S. P. Surface energies of elemental crystals. Sci. Data 2016, 3, 160080, DOI: 10.1038/sdata.2016.80Google Scholar34https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhsFCmsLzO&md5=146ff999161305aec429d5d682d40225Surface energies of elemental crystalsTran, Richard; Xu, Zihan; Radhakrishnan, Balachandran; Winston, Donald; Sun, Wenhao; Persson, Kristin A.; Ong, Shyue PingScientific Data (2016), 3 (), 160080CODEN: SDCABS; ISSN:2052-4463. (Nature Publishing Group)The surface energy is a fundamental property of the different facets of a crystal that is crucial to the understanding of various phenomena like surface segregation, roughening, catalytic activity, and the crystal's equil. shape. Such surface phenomena are esp. important at the nanoscale, where the large surface area to vol. ratios lead to properties that are significantly different from the bulk. In this work, we present the largest database of calcd. surface energies for elemental crystals to date. This database contains the surface energies of more than 100 polymorphs of about 70 elements, up to a max. Miller index of two and three for non-cubic and cubic crystals, resp. Well-known reconstruction schemes are also accounted for. The database is systematically improvable and has been rigorously validated against previous exptl. and computational data where available. We will describe the methodolgy used in constructing the database, and how it can be accessed for further studies and design of materials.
- 35Jain, A.; Ong, S. P.; Hautier, G.; Chen, W.; Richards, W. D.; Dacek, S.; Cholia, S.; Gunter, D.; Skinner, D.; Ceder, G.; Persson, K. A. Commentary: The materials project: A materials genome approach to accelerating materials innovation. APL Mater. 2013, 1, 011002, DOI: 10.1063/1.4812323Google Scholar35https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtlyktLjF&md5=88cb8642abed05e6b34a2191519b3ff3Commentary: The Materials Project: A materials genome approach to accelerating materials innovationJain, 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.
- 36Sholl, D. S. Density functional theory a practical introduction; Wiley: Hoboken, N.J, 2009.Google ScholarThere is no corresponding record for this reference.
- 37Xu, 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.1615733114Google Scholar37https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XitFWls7fM&md5=65404fc79b4b340710a0a4c287094cf0Enhanced strength and temperature dependence of mechanical properties of Li at small scales and its implications for Li metal anodesXu, 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.
- 38Owen, E. A.; Williams, G. I. X-ray measurements on lithium at low temperatures. Proc. Phys. Soc. Sect. A 1954, 67, 895– 900, DOI: 10.1088/0370-1298/67/10/306Google ScholarThere is no corresponding record for this reference.
- 39C Nash, H.; Smith, C. S. Single-crystal elastic constants of lithium. J. Phys. Chem. Solids 1959, 9, 113– 118, DOI: 10.1016/0022-3697(59)90201-xGoogle ScholarThere is no corresponding record for this reference.
- 40Trivisonno, J.; Smith, C. S. Elastic constants of lithium-magnesium alloys. Acta Metall 1961, 9, 1064– 1071, DOI: 10.1016/0001-6160(61)90175-4Google Scholar40https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaF38XjslCqsg%253D%253D&md5=e12b8b19b5c8f51c1010d5e603d25896Elastic constants of lithium-magnesium alloysTrivisonno, J.; Smith, Charles S.Acta Metallurgica (1961), 9 (), 1064-71CODEN: AMETAR; ISSN:0001-6160.The single-crystal elastic constants of dil. Li-Mg alloys were measured by using the ultrasonic pulse-echo technique. In terms of dln C÷dx, all fundamental elastic consts. increase with compn. by the following amts.: C44, 1.22; C', 1.03; B8, 1.20 per atom fraction. Small corrections for lattice parameter change upon alloying were made for C44 and C' by using the pressure derivatives of the elastic consts. of pure Li and the known variation of the lattice constant with compn. The remaining effect is ascribed to alloying alone. To understand both the elastic shear consts. of Li and their dependence on compn., one must include small neg. stiffness contributions arising in the Fermi energy, in addn. to the major and usual electrostatic stiffness. It is then possible to deduce the variation with compn. of each contribution from the measured total variation for the 2 shear consts., with the result that dln CE÷dx = 1.24 and dln CF÷dx 1.76.
- 41Slotwinski, T.; Trivisonno, J. Temperature dependence of the elastic constants of single crystal lithium. J. Phys. Chem. Solids 1969, 30, 1276– 1278, DOI: 10.1016/0022-3697(69)90386-2Google Scholar41https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaF1MXkt1Ggu7o%253D&md5=6b7a03ef66c7374ba5f05a48fdeee992Temperature dependence of the elastic constants of single crystal lithiumSlotwinski, T.; Trivisonno, J.Journal of Physics and Chemistry of Solids (1969), 30 (5), 1276-8CODEN: JPCSAW; ISSN:0022-3697.A direct measurement was made of the temp. dependence of the elastic const. of single-crystal Li at 78-190°K. The crystals were oriented by a Laue transmission technique and acoustic specimens were prepd. with orientations along the [110] and [100] directions. The relative and abs. velocity measurements were made by using an ultrasonic pulse echo technique with a Mg buffer rod between the acoustic specimen and the transducer. The temp. was varied by a small resistance heater and measured with a thermocouple. Once these measurements were completed, the crystals were immersed in liq. N and the values of the elastic const. were detd. at 78°K. The method used to det. the elastic const. from the measured sound velocities in various crystallographic directions is that given by J. R. Neighbours and C. S. Smith (1950). The temp. dependence of the elastic const. was detd. by making an abs. measurement at 90°K. and measuring the change in transit time as a function of temp. between 90 and 200°K. The system used was capable of measuring a 1-nsec. change in 10 μsec., so that the temp. dependence measurements could be made with high precision. The plots of the elastic const. vs. temp. are remarkably linear. The quantities (dC/dT)p are quite different for all the elastic const. This is to be expected not only because of the increased accuracy in transit time measurements, but also because the detn. of the temp. dependence was inferred from independent measurements at 3 different temps. The adiabatic bulk modulus, Bs, was computed from the 3 directly measured elastic const. The isothermal bulk modulus, Bt, was computed from Bs. The obtained Bt and Bs values, derived from ultrasonic data, are in agreement with earlier results obtained from isothermal compressional data.
- 42Masias, A.; Felten, N.; Garcia-Mendez, R.; Wolfenstine, J.; Sakamoto, J. Elastic, plastic, and creep mechanical properties of lithium metal. J. Mater. Sci. 2019, 54, 2585– 2600, DOI: 10.1007/s10853-018-2971-3Google Scholar42https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhvVGnurnM&md5=598bc2a4384e9457a9833ffcd44d5755Elastic, plastic, and creep mechanical properties of lithium metalMasias, Alvaro; Felten, Nando; Garcia-Mendez, Regina; Wolfenstine, Jeff; Sakamoto, JeffJournal of Materials Science (2019), 54 (3), 2585-2600CODEN: JMTSAS; ISSN:0022-2461. (Springer)With the potential to dramatically increase energy d. compared to conventional lithium ion technol., lithium metal solid-state batteries (LMSSB) have attracted significant attention. However, little is known about the mech. properties of Li. The purpose of this study was to characterize the elastic and plastic mech. properties and creep behavior of Li. Elastic properties were measured using an acoustic technique (pulse-echo). The Young's modulus, shear modulus, and Poisson's ratio were detd. to be 7.82 GPa, 2.83 GPa, and 0.381, resp. To characterize the stress-strain behavior of Li in tension and compression, a unique load frame was used inside an inert atm. The yield strength was detd. to be between 0.73 and 0.81 MPa. The time-dependent deformation in tension was dramatically different compared to compression. In tension, power law creep was exhibited with a stress exponent of 6.56, suggesting that creep was controlled by dislocation climb. In compression, time-dependent deformation was characterized over a range of stress believed to be germane to LMSSB (0.8-2.4 MPa). At all compressive stresses, significant barreling and a decrease in strain rate with increasing time were obsd. The implications of this observation on the charge/discharge behavior of LMSSB will be discussed. We believe the anal. and mech. properties measured in this work will help in the design and development of LMSSB.
- 43Beg, M. M.; Nielsen, M. Temperature dependence of lattice dynamics of lithium 7. Phys. Rev. B 1976, 14, 4266– 4273, DOI: 10.1103/PhysRevB.14.4266Google Scholar43https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaE2sXjs1Gmug%253D%253D&md5=bb70ba06c235e2ebe412e9ccf04d2941Temperature dependence of the lattice dynamics of lithium-7Beg, M. M.; Nielsen, M.Physical Review B: Solid State (1976), 14 (10), 4266-73CODEN: PLRBAQ; ISSN:0556-2805.Phonon dispersion relations in 7Li were measured by the coherent inelastic neutron scattering at 293 and 110 K. The frequency distributions were obtained from the exptl. data using the Born-von Karman general force model. The 1st-neighbor force consts. at 293 K are ∼10% smaller than those at 100 K. Temp. dependences of selected phonons were studied from 110 K to near the melting point. The energy shifts and phonon linewidths were evaluated at 293, 383, and 424 K by comparing the widths and energies to those measured at 110 K. The lattice parameter is 3.490 ± 0.003 Å at 110 K and 3.357 ± 0.003 Å at 424 K. The elastic consts. obtained at 293 K from the model parameters are (1011 dyne/cm2) C = 1.73 ± 0.10, C12 = 1.31 ± 0.20, and C44 = 0.84 ± 0.060. The temp. dependence of elastic const. was also detd.
- 44Wang, Y.; Dang, D.; Wang, M.; Xiao, X.; Cheng, Y.-T. Mechanical behavior of electroplated mossy lithium at room temperature studied by flat punch indentation. Appl. Phys. Lett. 2019, 115, 043903, DOI: 10.1063/1.5111150Google Scholar44https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhsVGrtLbN&md5=1863e241762fbcbee82c7f922b1a6628Mechanical behavior of electroplated mossy lithium at room temperature studied by flat punch indentationWang, Yikai; Dang, Dingying; Wang, Ming; Xiao, Xingcheng; Cheng, Yang-TseApplied Physics Letters (2019), 115 (4), 043903/1-043903/5CODEN: APPLAB; ISSN:0003-6951. (American Institute of Physics)We report the Young's modulus and deformation behavior of electroplated mossy Li at room temp. studied by flat punch indentation inside an Ar-filled glovebox. The Young's modulus of the mossy Li with a porosity of ∼62.3% is measured to be ∼2 GPa, which is smaller than that (∼7.8 GPa) of bulk Li. Both the mossy and bulk Li show clearly an indentation creep behavior. Despite its highly porous microstructure, the impression creep velocity of the mossy Li is less than one-thirtieth of that of bulk Li under the same stress. We propose possible mechanisms for the significantly higher deformation and creep resistance of the mossy Li over bulk Li. These findings are key to developing mech. suppression approaches to improve the cycling stability of Li metal electrodes. (c) 2019 American Institute of Physics.
- 45Zhang, H.; Li, C.; Djemia, P.; Yang, R.; Hu, Q. Prediction on temperature dependent elastic constants of “soft” metal Al by AIMD and QHA. J. Mater. Sci. Technol. 2020, 45, 92– 97, DOI: 10.1016/j.jmst.2019.11.029Google Scholar45https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XjtFyrsb%252FL&md5=0957e564f3914dfefc6d91378f71da33Prediction on temperature dependent elastic constants of "soft" metal Al by AIMD and QHAZhang, Haijun; Li, Chenhui; Djemia, Philippe; Yang, Rui; Hu, QingmiaoJournal of Materials Science & Technology (Shenyang, China) (2020), 45 (), 92-97CODEN: JSCTEQ; ISSN:1005-0302. (Editorial Board of Journal of Materials Science & Technology)First-principles methods based on d. functional theory (DFT)are nowadays routinely applied to calc. the elastic consts. of materials at temp. of 0 K. Nevertheless, the first-principles calcns. of elastic consts. at finite temp. are not straightforward. In the present work, the feasibility of the ab initio mol. dynamic (AIMD)method in calcns. of the temp. dependent elastic consts. of relatively "soft" metals, taking fcc. (FCC) aluminum (Al) as example, is explored. The AIMD calcns. are performed with carefully selected strain tensors and strain magnitude. In parallel with the AIMD calcns., first-principles calcns. with the quasiharmonic approxn. (QHA) are performed as well. We show that all three independent elastic const. components (C11, C12 and C44) of Al from both the AIMD and QHA calcns. decrease with increasing temp. T, in good agreement with those from exptl. measurements. Our work allows us to quantify the individual contributions of the vol. expansion, lattice vibration (excluding those contributed to the vol. expansion), and electronic temp. effects to the temp. induced variation of the elastic consts. For Al with stable FCC crystal structure, the vol. expansion effect contributes the major part (about 75%∼80%) in the temp. induced variation of the elastic consts. The contribution of the lattice vibration is minor (about 20%∼25%) while the electronic temp. effect is negligible. Although the elastic consts. soften with increasing temp., FCC Al satisfies the Born elastic stability criteria with temp. up to the exptl. m.p.
- 46Nye, J. F. Physical properties of crystals: their representation by tensors and matrices; Clarendon Press: Oxford, 1985.Google ScholarThere is no corresponding record for this reference.
- 47Jäckle, M.; Groß, A. Microscopic properties of lithium, sodium, and magnesium battery anode materials related to possible dendrite growth. J. Chem. Phys. 2014, 141, 174710, DOI: 10.1063/1.4901055Google Scholar47https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2M3mvFKqtg%253D%253D&md5=1a6b384f545454056968d9986c1d06bdMicroscopic properties of lithium, sodium, and magnesium battery anode materials related to possible dendrite growthJackle Markus; Gross AxelThe Journal of chemical physics (2014), 141 (17), 174710 ISSN:.Lithium and magnesium exhibit rather different properties as battery anode materials with respect to the phenomenon of dendrite formation which can lead to short-circuits in batteries. Diffusion processes are the key to understanding structure forming processes on surfaces. Therefore, we have determined adsorption energies and barriers for the self-diffusion on Li and Mg using periodic density functional theory calculations and contrasted the results to Na which is also regarded as a promising electrode material in batteries. According to our calculations, magnesium exhibits a tendency towards the growth of smooth surfaces as it exhibits lower diffusion barriers than lithium and sodium, and as an hcp metal it favors higher-coordinated configurations in contrast to the bcc metals Li and Na. These characteristic differences are expected to contribute to the unequal tendencies of these metals with respect to dendrite growth.
- 48Pande, V.; Viswanathan, V. Computational screening of current collectors for enabling anode-free lithium metal batteries. ACS Energy Lett. 2019, 4, 2952– 2959, DOI: 10.1021/acsenergylett.9b02306Google Scholar48https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitFaksL%252FM&md5=032bde91f3446ef848707f8783be4b8fComputational Screening of Current Collectors for Enabling Anode-Free Lithium Metal BatteriesPande, Vikram; Viswanathan, VenkatasubramanianACS Energy Letters (2019), 4 (12), 2952-2959CODEN: AELCCP; ISSN:2380-8195. (American Chemical Society)Li metal cells are key for achieving high specific energy for electrification of transportation and aviation. Anode-free cells are Li metal cells involving no excess Li with the highest possible specific energy. Anode-free cells are simpler, cheaper, and safer because they avoid the handling and manufg. of Li metal foils. The lack of excess Li magnifies issues related to dendrite growth and poor cycling in anode-free cells. The electrolyte and current collector surface play a crucial role in affecting anode-free cell cycling performance. The authors have computationally screened for candidate current collectors that nucleate Li effectively and allow uniform growth. These are detd. by the free energy of Li adsorption and Li surface diffusion barrier on candidate current collectors. Using d. functional theory calcns., Li alloys possess ideal characteristics for Li nucleation and growth. These can lead to vastly improved performance compared to current transition-metal current collectors.
- 49Henkelman, G.; Uberuaga, B. P.; Jónsson, H. A climbing image nudged elastic band method for finding saddle points and minimum energy paths. J. Chem. Phys. 2000, 113, 9901– 9904, DOI: 10.1063/1.1329672Google Scholar49https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXosFagurc%253D&md5=3899b9e2e9e3eb74009987d96623f018A climbing image nudged elastic band method for finding saddle points and minimum energy pathsHenkelman, Graeme; Uberuaga, Blas P.; Jonsson, HannesJournal of Chemical Physics (2000), 113 (22), 9901-9904CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)A modification of the nudged elastic band method for finding min. energy paths is presented. One of the images is made to climb up along the elastic band to converge rigorously on the highest saddle point. Also, variable spring consts. are used to increase the d. of images near the top of the energy barrier to get an improved est. of the reaction coordinate near the saddle point. Applications to CH4 dissociative adsorption on Ir(111) and H2 on Si(100) using plane wave based d. functional theory are presented.
- 50Bell, R. P.; Hinshelwood, C. N. The theory of reactions involving proton transfers. Proc. R. Soc. Lond. Ser. A Math. Phys. Sci. 1936, 154, 414– 429, DOI: 10.1908/rspa.1936.0060Google ScholarThere is no corresponding record for this reference.
- 51Evans, M. G.; Polanyi, M. Further considerations on the thermodynamics of chemical equilibria and reaction rates. Trans. Faraday Soc. 1936, 32, 1333– 1360, DOI: 10.1039/tf9363201333Google Scholar51https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaA2sXisV2k&md5=8c2aa44e8a3764c445fc2d41d0c664bcFurther considerations of the thermodynamics of chemical equilibria and reaction ratesEvans, M. G.; Polanyi, M.Transactions of the Faraday Society (1936), 32 (), 1333-60CODEN: TFSOA4; ISSN:0014-7672.cf. C. A. 30, 3703.5. Theoretical discussion of the transition state, the effect of changes in mol. structure and of solvent, the relation between heats and entropies of soln., collision factors in soln., viscosity of the solvent, connection with the Nernst theorem and activation energies derived from energy surfaces.
- 52Schwoebel, R. L.; Shipsey, E. J. Step motion on crystal surfaces. J. Appl. Phys. 1966, 37, 3682– 3686, DOI: 10.1063/1.1707904Google Scholar52https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaF28XkslOrs78%253D&md5=6e30ed5ad8c0c080aa8b28477612f4e1Step motion on crystal surfacesSchwoebel, Richard L.; Shipsey, Edward J.Journal of Applied Physics (1966), 37 (10), 3682-6CODEN: JAPIAU; ISSN:0021-8979.Steps on crystal surfaces capture atoms diffusing on the surface with certain probabilities and, in addn., the capture probability depends on the direction from which adsorbed atoms approach the step. A general solution for the time-dependent step distribution is obtained in terms of these probabilities and an arbitrary initial distribution of an infinite sequence of parallel steps. Coalescence of steps or stabilization of step spacings can occur as a consequence of assuming that capture probabilities are directionally dependent. Some of the implications of the theoretical model are related to the growth of real crystal surfaces.
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Abstract
Figure 1
Figure 1. Schematic showing the process by which data were generated and potentials were validated.
Figure 2
Figure 2. Bulk mechanical properties of lithium as a function of temperature calculated using the NequIP32 and MEAM potential and compared to the experimental results (38−41) and the quasiharmonic approximation (QHA) (37) where possible. (a) Lattice constant as a function of temperature with error bars as standard deviations of the volume fluctuation in the NPT simulation. (b–d) C11, C12, and C44 elastic constants, respectively, with error bars as the standard error from the fitting of stress–strain curves. Note how the QHA fails to capture the behavior of C44. (e) Elastic anisotropy with error bars propagated from errors in the elastic constants. (f–i) Voigt–Reuss–Hill averaged bulk, shear, and Young’s moduli and the Poisson ratio, respectively.
Figure 3
Figure 3. Various surface properties calculated using the NequIP64 potential. (a) Demonstration of a BEP correlation between the adsorption energy and the minimum diffusion barrier of each facet. The (111) surface was not included in the fitting of the dotted line. (b) Calculation of the Ehrlich–Schwöebel barrier matching results in the literature. (c) A variety of SPESs colored by the relative adsorption energy show the different geometries of adsorption sites and the minimum diffusion barrier paths adatoms can take when diffusing from one surface unit cell to the next.
References
This article references 52 other publications.
- 1Wang, R.; Cui, W.; Chu, F.; Wu, F. Lithium metal anodes: Present and future. J. Energy Chem. 2020, 48, 145– 159, DOI: 10.1016/j.jechem.2019.12.024There is no corresponding record for this reference.
- 2Zhu, Y.; Pande, V.; Li, L.; Wen, B.; Pan, M. S.; Wang, D.; Ma, Z.-F.; Viswanathan, V.; Chiang, Y.-M. Design principles for self-forming interfaces enabling stable lithium-metal anodes. Proc. Natl. Acad. Sci. U. S. A. 2020, 117, 27195– 27203, DOI: 10.1073/pnas.20019231172https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXit1Ghu73O&md5=4972a5c510ab41f42bead48485734d73Design principles for self-forming interfaces enabling stable lithium-metal anodesZhu, Yingying; Pande, Vikram; Li, Linsen; Wen, Bohua; Pan, Menghsuan Sam; Wang, David; Ma, Zi-Feng; Viswanathan, Venkatasubramanian; Chiang, Yet-MingProceedings of the National Academy of Sciences of the United States of America (2020), 117 (44), 27195-27203CODEN: PNASA6; ISSN:0027-8424. (National Academy of Sciences)The path toward Li-ion batteries with higher energy densities will likely involve use of thin lithium (Li)-metal anode (<50 Aμm thickness), whose cyclability today remains limited by dendrite formation and low coulombic efficiency (CE). Previous studies have shown that the solid-electrolyte interface (SEI) of the Li metal plays a crucial role in Li-electrodeposition and -stripping behavior. However, design rules for optimal SEIs are not well established. Here, using integrated exptl. and modeling studies on a series of structurally similar SEI-modifying model compds., we reveal the relationship between SEI compns., Li deposition morphol., and CE and identify two key descriptors for the fraction of ionic compds. and compactness, leading to high-performance SEIs. We further demonstrate one of the longest cycle lives to date (350 cycles for 80% capacity retention) for a high specific-energy Li||LiCoO2 full cell (projected >350 W hours [Wh]/kg) at practical current densities. Our results provide guidance for rational design of the SEI to further improve Li-metal anodes.
- 3Lin, D.; Liu, Y.; Cui, Y. Reviving the lithium metal anode for high-energy batteries. Nat. Nanotechnol. 2017, 12, 194– 206, DOI: 10.1038/nnano.2017.163https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXmtVyitr4%253D&md5=07d29fc449ccc1f6c941b4c7692a8639Reviving the lithium metal anode for high-energy batteriesLin, Dingchang; Liu, Yayuan; Cui, YiNature 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.
- 4Monroe, C.; Newman, J. The impact of elastic deformation on deposition kinetics at lithium/polymer interfaces. J. Electrochem. Soc. 2005, 152, A396, DOI: 10.1149/1.18508544https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2MXhs1KktLc%253D&md5=e878820c6a811396757bd7435bdb40adThe impact of elastic deformation on deposition kinetics at lithium/polymer interfacesMonroe, Charles; Newman, JohnJournal 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.
- 5Ahmad, 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.119.0560035https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhs1SrtbrP&md5=b71331ba73f970ec5c45f52cdc08ecadStability of electrodeposition at solid-solid interfaces and implications for metal anodesAhmad, Zeeshan; Viswanathan, VenkatasubramanianPhysical 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.
- 6Jäckle, M.; Helmbrecht, K.; Smits, M.; Stottmeister, D.; Groß, A. Self-diffusion barriers: possible descriptors for dendrite growth in batteries?. Energy Environ. Sci. 2018, 11, 3400– 3407, DOI: 10.1039/C8EE01448EThere is no corresponding record for this reference.
- 7Gaissmaier, D.; Fantauzzi, D.; Jacob, T. First principles studies of self-diffusion processes on metallic lithium surfaces. J. Chem. Phys. 2019, 150, 041723, DOI: 10.1063/1.50562267https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXis1Sntr3O&md5=3e3a043b22b3e0fc88f9c4240f84b885First principles studies of self-diffusion processes on metallic lithium surfacesGaissmaier, Daniel; Fantauzzi, Donato; Jacob, TimoJournal of Chemical Physics (2019), 150 (4), 041723/1-041723/14CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)Due to the theor. high specific capacity (3860 mAh/g) and the low std. electrode potential (-3.040 V vs. std. hydrogen electrode), rechargeable lithium metal batteries are considered as excellent energy storage systems. Unfortunately, security concerns related to dendrite formation during charge/discharge cycles still hinder the com. use of Li metal-based batteries. Using d. functional theory, the bulk and surface properties are studied of metallic lithium at an atomistic level. In this process, body-centered cubic Li(100) proved to be the most stable metallic lithium surface. Subsequently, possible self-diffusion mechanisms on perfect and imperfect Li(100) surfaces were examd. For this purpose, nudged elastic band calcns. were performed to characterize the resp. diffusion processes and to det. the relevant pre-exponential factors and activation barriers. On the basis of the acquired data, it became possible to derive activation temps. and reaction rates for the resp. processes, which are useful for exptl. verification as well as for the implementation in long-scale kinetic Monte Carlo simulations. (c) 2019 American Institute of Physics.
- 8Li, 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.aam60148https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhslSgsb7O&md5=313b19162d2034ecf59cdef499b39565Atomic structure of sensitive battery materials and interfaces revealed by cryo-electron microscopyLi, 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, YiScience (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.
- 9Kresse, G.; Furthmüller, J. Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis set. Comput. Mater. Sci. 1996, 6, 15– 50, DOI: 10.1016/0927-0256(96)00008-09https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK28XmtFWgsrk%253D&md5=779b9a71bbd32904f968e39f39946190Efficiency of ab-initio total energy calculations for metals and semiconductors using a plane-wave basis setKresse, G.; Furthmuller, J.Computational Materials Science (1996), 6 (1), 15-50CODEN: CMMSEM; ISSN:0927-0256. (Elsevier)The authors present a detailed description and comparison of algorithms for performing ab-initio quantum-mech. calcns. using pseudopotentials and a plane-wave basis set. The authors will discuss: (a) partial occupancies within the framework of the linear tetrahedron method and the finite temp. d.-functional theory, (b) iterative methods for the diagonalization of the Kohn-Sham Hamiltonian and a discussion of an efficient iterative method based on the ideas of Pulay's residual minimization, which is close to an order N2atoms scaling even for relatively large systems, (c) efficient Broyden-like and Pulay-like mixing methods for the charge d. including a new special preconditioning optimized for a plane-wave basis set, (d) conjugate gradient methods for minimizing the electronic free energy with respect to all degrees of freedom simultaneously. The authors have implemented these algorithms within a powerful package called VAMP (Vienna ab-initio mol.-dynamics package). The program and the techniques have been used successfully for a large no. of different systems (liq. and amorphous semiconductors, liq. simple and transition metals, metallic and semi-conducting surfaces, phonons in simple metals, transition metals and semiconductors) and turned out to be very reliable.
- 10Behler, J.; Csányi, G. Machine learning potentials for extended systems: a perspective. Eur. Phys. J. B 2021, 94, 142, DOI: 10.1140/epjb/s10051-021-00156-110https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3MXhsFylsbjN&md5=6fc466cc5c9769e0f7e3a353e9b4bac7Machine learning potentials for extended systems: a perspectiveBehler, Joerg; Csanyi, GaborEuropean Physical Journal B: Condensed Matter and Complex Systems (2021), 94 (7), 142CODEN: EPJBFY; ISSN:1434-6028. (Springer)Abstr.: In the past two and a half decades machine learning potentials have evolved from a special purpose soln. to a broadly applicable tool for large-scale atomistic simulations. By combining the efficiency of empirical potentials and force fields with an accuracy close to first-principles calcns. they now enable computer simulations of a wide range of mols. and materials. In this perspective, we summarize the present status of these new types of models for extended systems, which are increasingly used for materials modeling. There are several approaches, but they all have in common that they exploit the locality of at. properties in some form. Long-range interactions, most prominently electrostatic interactions, can also be included even for systems in which non-local charge transfer leads to an electronic structure that depends globally on all at. positions. Remaining challenges and limitations of current approaches are discussed. Graphic Abstr.: [graphic not available: see fulltext].
- 11Batzner, S.; Musaelian, A.; Sun, L.; Geiger, M.; Mailoa, J. P.; Kornbluth, M.; Molinari, N.; Smidt, T. E.; Kozinsky, B. E. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nat. Commun. 2022, 13, 2453, DOI: 10.1038/s41467-022-29939-511https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38Xht1ShtLrE&md5=63e6cc63d7c47859c90f929e5baea6f5E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentialsBatzner, Simon; Musaelian, Albert; Sun, Lixin; Geiger, Mario; Mailoa, Jonathan P.; Kornbluth, Mordechai; Molinari, Nicola; Smidt, Tess E.; Kozinsky, BorisNature Communications (2022), 13 (1), 2453CODEN: NCAOBW; ISSN:2041-1723. (Nature Portfolio)This work presents Neural Equivariant Interat. Potentials (NequIP), an E(3)-equivariant neural network approach for learning interat. potentials from ab-initio calcns. for mol. dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP employs E(3)-equivariant convolutions for interactions of geometric tensors, resulting in a more information-rich and faithful representation of at. environments. The method achieves state-of-the-art accuracy on a challenging and diverse set of mols. and materials while exhibiting remarkable data efficiency. NequIP outperforms existing models with up to three orders of magnitude fewer training data, challenging the widely held belief that deep neural networks require massive training sets. The high data efficiency of the method allows for the construction of accurate potentials using high-order quantum chem. level of theory as ref. and enables high-fidelity mol. dynamics simulations over long time scales.
- 12Schütt, K. T.; Sauceda, H. E.; Kindermans, P.-J.; Tkatchenko, A.; Müller, K. R. SchNet – A deep learning architecture for molecules and materials. J. Chem. Phys. 2018, 148, 241722, DOI: 10.1063/1.501977912https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXms1Ggurs%253D&md5=988638d520a423f529a16b35031243aaSchNet - A deep learning architecture for molecules and materialsSchuett, K. T.; Sauceda, H. E.; Kindermans, P.-J.; Tkatchenko, A.; Mueller, K.-R.Journal of Chemical Physics (2018), 148 (24), 241722/1-241722/11CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chem. physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mech. interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chem. compd. space. Here, we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chem. space for mols. and materials, where our model learns chem. plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for mol. dynamics simulations of small mols. and perform an exemplary study on the quantum-mech. properties of C20-fullerene that would have been infeasible with regular ab initio mol. dynamics. (c) 2018 American Institute of Physics.
- 13Cheng, B.; Mazzola, G.; Pickard, C. J.; Ceriotti, M. Evidence for supercritical behaviour of high-pressure liquid hydrogen. Nature 2020, 585, 217– 220, DOI: 10.1038/s41586-020-2677-y13https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhvFSlt7fM&md5=c835bf1d7e5af0f98636cc015de23d42Evidence for supercritical behaviour of high-pressure liquid hydrogenCheng, Bingqing; Mazzola, Guglielmo; Pickard, Chris J.; Ceriotti, MicheleNature (London, United Kingdom) (2020), 585 (7824), 217-220CODEN: NATUAS; ISSN:0028-0836. (Nature Research)Hydrogen, the simplest and most abundant element in the Universe, develops a remarkably complex behavior upon compression. Since Wigner predicted the dissocn. and metalization of solid hydrogen at megabar pressures almost a century ago, several efforts have been made to explain the many unusual properties of dense hydrogen, including a rich and poorly understood solid polymorphism, an anomalous melting line and the possible transition to a superconducting state. Expts. at such extreme conditions are challenging and often lead to hard-to-interpret and controversial observations, whereas theor. investigations are constrained by the huge computational cost of sufficiently accurate quantum mech. calcns. Here we present a theor. study of the phase diagram of dense hydrogen that uses machine learning to 'learn' potential-energy surfaces and interat. forces from ref. calcns. and then predict them at low computational cost, overcoming length- and timescale limitations. We reproduce both the re-entrant melting behavior and the polymorphism of the solid phase. Simulations using our machine-learning-based potentials provide evidence for a continuous mol.-to-at. transition in the liq., with no first-order transition obsd. above the melting line. This suggests a smooth transition between insulating and metallic layers in giant gas planets, and reconciles existing discrepancies between expts. as a manifestation of supercrit. behavior.
- 14Freitas, R.; Cao, Y. Machine-learning potentials for crystal defects. MRS Commun. 2022, 12, 510– 520, DOI: 10.1557/s43579-022-00221-514https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XitFartLfO&md5=91109e3214f37917c8a991af24261678Machine-learning potentials for crystal defectsFreitas, Rodrigo; Cao, YifanMRS Communications (2022), 12 (5), 510-520CODEN: MCROF8; ISSN:2159-6867. (Springer International Publishing AG)Abstr.: Decades of advancements in strategies for the calcn. of at. interactions have culminated in a class of methods known as machine-learning interat. potentials (MLIAPs). MLIAPs dramatically widen the spectrum of materials systems that can be simulated with high phys. fidelity, including their microstructural evolution and kinetics. This framework, in conjunction with cross-scale simulations and in silico microscopy, is poised to bring a paradigm shift to the field of atomistic simulations of materials. In this prospective article we summarize recent progress in the application of MLIAPs to crystal defects. Graphical abstr.: [graphic not available: see fulltext].
- 15Owen, C. J.; Torrisi, S. B.; Xie, Y.; Batzner, S.; Coulter, J.; Musaelian, A.; Sun, L.; Kozinsky, B. Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set. arXiv 2023, arXiv:2302.12993, DOI: 10.48550/arXiv.2302.12993There is no corresponding record for this reference.
- 16Zuo, Y.; Chen, C.; Li, X.; Deng, Z.; Chen, Y.; Behler, J.; Csányi, G.; Shapeev, A. V.; Thompson, A. P.; Wood, M. A.; Ong, S. P. Performance and cost assessment of machine learning interatomic potentials. J. Phys. Chem. A 2020, 124, 731– 745, DOI: 10.1021/acs.jpca.9b0872316https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXmtVKjsg%253D%253D&md5=7716fe55d3269109bfc101fdfc25d823Performance and Cost Assessment of Machine Learning Interatomic PotentialsZuo, Yunxing; Chen, Chi; Li, Xiangguo; Deng, Zhi; Chen, Yiming; Behler, Jorg; Csanyi, Gabor; Shapeev, Alexander V.; Thompson, Aidan P.; Wood, Mitchell A.; Ong, Shyue PingJournal of Physical Chemistry A (2020), 124 (4), 731-745CODEN: JPCAFH; ISSN:1089-5639. (American Chemical Society)Machine learning of the quant. relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interat. potentials (IAPs). Here, we present a comprehensive evaluation of machine learning IAPs (ML-IAPs) based on four local environment descriptors-atom-centered symmetry functions (ACSF), smooth overlap of at. positions (SOAP), the spectral neighbor anal. potential (SNAP) bispectrum components, and moment tensors-using a diverse data set generated using high-throughput d. functional theory (DFT) calcns. The data set comprising bcc (Li, Mo) and fcc (Cu, Ni) metals and diamond group IV semiconductors (Si, Ge) is chosen to span a range of crystal structures and bonding. All descriptors studied show excellent performance in predicting energies and forces far surpassing that of classical IAPs, as well as predicting properties such as elastic consts. and phonon dispersion curves. We observe a general trade-off between accuracy and the degrees of freedom of each model and, consequently, computational cost. We will discuss these trade-offs in the context of model selection for mol. dynamics and other applications.
- 17Zhang, L.; Han, J.; Wang, H.; Car, R.; Weinan, E. Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics. Phys. Rev. Lett. 2018, 120, 143001, DOI: 10.1103/PhysRevLett.120.14300117https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXltFSksrg%253D&md5=8b3f87603d7e1ea708341862744576e1Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum MechanicsZhang, Linfeng; Han, Jiequn; Wang, Han; Car, Roberto; E, WeinanPhysical Review Letters (2018), 120 (14), 143001CODEN: PRLTAO; ISSN:1079-7114. (American Physical Society)We introduce a scheme for mol. simulations, the deep potential mol. dynamics (DPMD) method, based on a many-body potential and interat. forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and mols. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.
- 18Jiao, J.; Lai, G.; Zhao, L.; Lu, J.; Li, Q.; Xu, X.; Jiang, Y.; He, Y.-B.; Ouyang, C.; Pan, F.; Li, H.; Zheng, J. Self-healing mechanism of lithium in lithium metal. Adv. Sci. 2022, 9, 2105574, DOI: 10.1002/advs.20210557418https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XhtVGisLvJ&md5=d4890c132daf3a04d29b86499a13b782Self-Healing Mechanism of Lithium in Lithium MetalJiao, Junyu; Lai, Genming; Zhao, Liang; Lu, Jiaze; Li, Qidong; Xu, Xianqi; Jiang, Yao; He, Yan-Bing; Ouyang, Chuying; Pan, Feng; Li, Hong; Zheng, JiaxinAdvanced Science (Weinheim, Germany) (2022), 9 (12), 2105574CODEN: ASDCCF; ISSN:2198-3844. (Wiley-VCH Verlag GmbH & Co. KGaA)Li is an ideal anode material for use in state-of-the-art secondary batteries. However, Li-dendrite growth is a safety concern and results in low coulombic efficiency, which significantly restricts the com. application of Li secondary batteries. Unfortunately, the Li-deposition (growth) mechanism is poorly understood on the at. scale. Here, machine learning is used to construct a Li potential model with quantum-mech. computational accuracy. Mol. dynamics simulations in this study with this model reveal two self-healing mechanisms in a large Li-metal system, viz. surface self-healing, and bulk self-healing. It is concluded that self-healing occurs rapidly in nanoscale; thus, minimizing the voids between the Li grains using several comprehensive methods can effectively facilitate the formation of dendrite-free Li.
- 19Musaelian, A.; Batzner, S.; Johansson, A.; Sun, L.; Owen, C. J.; Kornbluth, M.; Kozinsky, B. Learning local equivariant representations for large-scale atomistic dynamics. arXiv 2022, arXiv:2204.05249There is no corresponding record for this reference.
- 20Batatia, I.; Batzner, S.; Kovács, D. P.; Musaelian, A.; Simm, G. N. C.; Drautz, R.; Ortner, C.; Kozinsky, B.; Csányi, G. The design space of E(3)-equivariant atom-centered interatomic potentials. arXiv 2022, arXiv:2205.06643, DOI: 10.48550/arXiv.2205.06643There is no corresponding record for this reference.
- 21Behler, J.; Parrinello, M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys. Rev. Lett. 2007, 98, 146401, DOI: 10.1103/PhysRevLett.98.14640121https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXjvF2ls7w%253D&md5=579a6cbf503565205acbb86ade0ae86bGeneralized Neural-Network Representation of High-Dimensional Potential-Energy SurfacesBehler, Jorg; Parrinello, MichelePhysical Review Letters (2007), 98 (14), 146401/1-146401/4CODEN: PRLTAO; ISSN:0031-9007. (American Physical Society)The accurate description of chem. processes often requires the use of computationally demanding methods like d.-functional theory (DFT), making long simulations of large systems unfeasible. In this Letter we introduce a new kind of neural-network representation of DFT potential-energy surfaces, which provides the energy and forces as a function of all at. positions in systems of arbitrary size and is several orders of magnitude faster than DFT. The high accuracy of the method is demonstrated for bulk silicon and compared with empirical potentials and DFT. The method is general and can be applied to all types of periodic and nonperiodic systems.
- 22Cubuk, E. D.; Malone, B. D.; Onat, B.; Waterland, A.; Kaxiras, E. Representations in neural network based empirical potentials. J. Chem. Phys. 2017, 147, 024104, DOI: 10.1063/1.499050322https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2sXhtFKrsrrM&md5=ac6353696a39a0dc0251f6c08c3134f0Representations in neural network based empirical potentialsCubuk, Ekin D.; Malone, Brad D.; Onat, Berk; Waterland, Amos; Kaxiras, EfthimiosJournal of Chemical Physics (2017), 147 (2), 024104/1-024104/5CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)Many structural and mech. properties of crystals, glasses, and biol. macromols. can be modeled from the local interactions between atoms. These interactions ultimately derive from the quantum nature of electrons, which can be prohibitively expensive to simulate. Machine learning has the potential to revolutionize materials modeling due to its ability to efficiently approx. complex functions. For example, neural networks can be trained to reproduce results of d. functional theory calcns. at a much lower cost. However, how neural networks reach their predictions is not well understood, which has led to them being used as a "black box" tool. This lack of understanding is not desirable esp. for applications of neural networks in scientific inquiry. We argue that machine learning models trained on phys. systems can be used as more than just approxns. since they had to "learn" phys. concepts in order to reproduce the labels they were trained on. We use dimensionality redn. techniques to study in detail the representation of silicon atoms at different stages in a neural network, which provides insight into how a neural network learns to model at. interactions. (c) 2017 American Institute of Physics.
- 23Giannozzi, P.; Baroni, S.; Bonini, N.; Calandra, M.; Car, R.; Cavazzoni, C.; Ceresoli, D.; Chiarotti, G. L.; Cococcioni, M.; Dabo, I. QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials. J. Phys.: Condens. Matter 2009, 21, 395502, DOI: 10.1088/0953-8984/21/39/39550223https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3Mjltl2lug%253D%253D&md5=da053fa748721b6b381051a20e7a7f53QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materialsGiannozzi Paolo; Baroni Stefano; Bonini Nicola; Calandra Matteo; Car Roberto; Cavazzoni Carlo; Ceresoli Davide; Chiarotti Guido L; Cococcioni Matteo; Dabo Ismaila; Dal Corso Andrea; de Gironcoli Stefano; Fabris Stefano; Fratesi Guido; Gebauer Ralph; Gerstmann Uwe; Gougoussis Christos; Kokalj Anton; Lazzeri Michele; Martin-Samos Layla; Marzari Nicola; Mauri Francesco; Mazzarello Riccardo; Paolini Stefano; Pasquarello Alfredo; Paulatto Lorenzo; Sbraccia Carlo; Scandolo Sandro; Sclauzero Gabriele; Seitsonen Ari P; Smogunov Alexander; Umari Paolo; Wentzcovitch Renata MJournal of physics. Condensed matter : an Institute of Physics journal (2009), 21 (39), 395502 ISSN:.QUANTUM ESPRESSO is an integrated suite of computer codes for electronic-structure calculations and materials modeling, based on density-functional theory, plane waves, and pseudopotentials (norm-conserving, ultrasoft, and projector-augmented wave). The acronym ESPRESSO stands for opEn Source Package for Research in Electronic Structure, Simulation, and Optimization. It is freely available to researchers around the world under the terms of the GNU General Public License. QUANTUM ESPRESSO builds upon newly-restructured electronic-structure codes that have been developed and tested by some of the original authors of novel electronic-structure algorithms and applied in the last twenty years by some of the leading materials modeling groups worldwide. Innovation and efficiency are still its main focus, with special attention paid to massively parallel architectures, and a great effort being devoted to user friendliness. QUANTUM ESPRESSO is evolving towards a distribution of independent and interoperable codes in the spirit of an open-source project, where researchers active in the field of electronic-structure calculations are encouraged to participate in the project by contributing their own codes or by implementing their own ideas into existing codes.
- 24Perdew, J. P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple [Phys. Rev. Lett. 77, 3865 (1996)]. Phys. Rev. Lett. 1997, 78, 1396, DOI: 10.1103/PhysRevLett.78.139624https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaK2sXht1Gns7o%253D&md5=ecdb6e129b112a3a10e08cba26a083aeGeneralized gradient approximation made simple. [Erratum to document cited in CA126:51093]Perdew, John P.; Burke, Kieron; Ernzerhof, MatthiasPhysical Review Letters (1997), 78 (7), 1396CODEN: PRLTAO; ISSN:0031-9007. (American Physical Society)The errors were not reflected in the abstr. or the index entries.
- 25Blöchl, P. E. Projector augmented-wave method. Phys. Rev. B 1994, 50, 17953– 17979, DOI: 10.1103/PhysRevB.50.1795325https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2sfjslSntA%253D%253D&md5=1853d67af808af2edab58beaab5d3051Projector augmented-wave methodBlochlPhysical review. B, Condensed matter (1994), 50 (24), 17953-17979 ISSN:0163-1829.There is no expanded citation for this reference.
- 26Monkhorst, H. J.; Pack, J. D. Special points for Brillouin-zone integrations. Phys. Rev. B 1976, 13, 5188– 5192, DOI: 10.1103/PhysRevB.13.5188There is no corresponding record for this reference.
- 27Methfessel, M.; Paxton, A. T. High-precision sampling for Brillouin-zone integration in metals. Phys. Rev. B 1989, 40, 3616– 3621, DOI: 10.1103/PhysRevB.40.361627https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaL1MXls1Slsr0%253D&md5=f10d684acee27eebaad6f576283d0310High-precision sampling for Brillouin-zone integration in metalsMethfessel, M.; Paxton, A. T.Physical Review B: Condensed Matter and Materials Physics (1989), 40 (6), 3616-21CODEN: PRBMDO; ISSN:0163-1829.A sampling method is given for Brillouin-zone integration in metals which converges exponentially with the no. of sampling points, without the loss of precision of normal broadening techniques. The scheme is based on smooth approximants to the δ and step functions which are constructed to give the exact result when integrating polynomials of a prescribed degree. In applications to the simple-cubic tight-binding band as well as to band structures of simple and transition metals, significant improvement over existing methods was shown. The method promises general applicability in the fields of total-energy calcns. and many-body physics.
- 28Zhang, Y.; Wang, H.; Chen, W.; Zeng, J.; Zhang, L.; Wang, H.; Weinan, E. DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models. Comput. Phys. Commun. 2020, 253, 107206, DOI: 10.1016/j.cpc.2020.10720628https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXjvFWjsrk%253D&md5=b803015c29378bc1d4bd8a2b4631c4ebDP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy modelsZhang, Yuzhi; Wang, Haidi; Chen, Weijie; Zeng, Jinzhe; Zhang, Linfeng; Wang, Han; E, WeinanComputer Physics Communications (2020), 253 (), 107206CODEN: CPHCBZ; ISSN:0010-4655. (Elsevier B.V.)In recent years, promising deep learning based interat. potential energy surface (PES) models have been proposed that can potentially allow us to perform mol. dynamics simulations for large scale systems with quantum accuracy. However, making these models truly reliable and practically useful is still a very non-trivial task. A key component in this task is the generation of datasets used in model training. In this paper, we introduce the Deep Potential GENerator (DP-GEN), an open-source software platform that implements the recently proposed "on-the-fly" learning procedure (Zhang et al. 2019) and is capable of generating uniformly accurate deep learning based PES models in a way that minimizes human intervention and the computational cost for data generation and model training. DP-GEN automatically and iteratively performs three steps: exploration, labeling, and training. It supports various popular packages for these three steps: LAMMPS for exploration, Quantum Espresso, VASP, CP2K, etc. for labeling, and DeePMD-kit for training. It also allows automatic job submission and result collection on different types of machines, such as high performance clusters and cloud machines, and is adaptive to different job management tools, including Slurm, PBS, and LSF. As a concrete example, we illustrate the details of the process for generating a general-purpose PES model for Cu using DP-GEN. Program Title: DP-GENProgram Files doi:http://dx.doi.org/10.17632/sxybkgc5xc.1Licensing provisions: LGPLProgramming language: PythonNature of problem: Generating reliable deep learning based potential energy models with minimal human intervention and computational cost. Soln. method: The concurrent learning scheme is implemented. Supports for sampling configuration space with LAMMPS, generating ab initio data with Quantum Espresso, VASP, CP2K and training potential models with DeePMD-kit are provided. Supports for different machines including workstations, high performance clusters and cloud machines are provided. Supports for job management tools including Slurm, PBS, LSF are provided.
- 29Shinoda, W.; Shiga, M.; Mikami, M. Rapid estimation of elastic constants by molecular dynamics simulation under constant stress. Phys. Rev. B 2004, 69, 134103, DOI: 10.1103/PhysRevB.69.13410329https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2cXjvVWhtbY%253D&md5=de5cee06e65c288c55d2b57b9f3a62c2Rapid estimation of elastic constants by molecular dynamics simulation under constant stressShinoda, Wataru; Shiga, Motoyuki; Mikami, MasuhiroPhysical Review B: Condensed Matter and Materials Physics (2004), 69 (13), 134103/1-134103/8CODEN: PRBMDO; ISSN:0163-1829. (American Physical Society)Mol. simulations, when they are used to understand properties characterizing the mech. strength of solid materials, such as stress-strain relation or Born stability criterion, by using elastic consts., are sometimes seriously time consuming. In order to resolve this problem, we propose an efficient simulation approach under const. external stress and temp., modifying Parrinello-Rahman (PR) method using useful sampling techniques developed recently-massive Nose-Hoover chain method and hybrid Monte Carlo method. Test calcns. on the Ni crystal employing the embedded atom method have shown that our method greatly improved the efficiency in sampling the elastic properties compared with the conventional PR method.
- 30Kim, Y.-M.; Jung, I.-H.; Lee, B.-J. Atomistic modeling of pure Li and Mg–Li system. Modell. Simul. Mater. Sci. Eng. 2012, 20, 035005, DOI: 10.1088/0965-0393/20/3/03500530https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC38XmtVGqtr8%253D&md5=7e72312166b94c9dcd0feabea079c917Atomistic modeling of pure Li and Mg-Li systemKim, Young-Min; Jung, In-Ho; Lee, Byeong-JooModelling and Simulation in Materials Science and Engineering (2012), 20 (3), 035005/1-035005/13CODEN: MSMEEU; ISSN:1361-651X. (Institute of Physics Publishing)Interat. potentials for pure Li and the Mg-Li binary system have been developed based on the second nearest-neighbor modified embedded-atom method formalism. The potentials can describe various fundamental phys. properties of pure Li (bulk, point defect, planar defect and thermal properties) and alloy behaviors (thermodn., structural and elastic properties) in reasonable agreement with exptl. data or higher-level calcns. The applicability of the potential to atomistic investigations on the deformation behavior of Mg alloys and the effect of Li is demonstrated.
- 31Luo, S.; Zhang, Y.; Liu, X.; Wang, Z.; Fan, A.; Wang, H.; Ma, W.; Zhu, L.; Zhang, X. Thermal behavior of Li electrode in all-solid-state batteries and improved performance by temperature modulation. Int. J. Heat Mass Transfer 2022, 199, 123450, DOI: 10.1016/j.ijheatmasstransfer.2022.12345031https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XisF2iur7L&md5=a050d54d301a023ddc76fda546ca4c43Thermal behavior of Li electrode in all-solid-state batteries and improved performance by temperature modulationLuo, Shuting; Zhang, Yufeng; Liu, Xinyu; Wang, Zhenyu; Fan, Aoran; Wang, Haidong; Ma, Weigang; Zhu, Lingyun; Zhang, XingInternational Journal of Heat and Mass Transfer (2022), 199 (), 123450CODEN: IJHMAK; ISSN:0017-9310. (Elsevier Ltd.)All-solid-state lithium metal batteries (ASSLMBs) hold tremendous promise in elec. vehicles and portable electronic devices owing to the remarkably improved safety and energy d. However, the uncontrollable Li dendrites seriously hinder the cycling performance of ASSLMBs, and the fickle environment urgently requires a reliable energy supply over a wide temp. range. Understanding the temp.-dependent behavior of Li metal electrode holds great significance for breaking the bottleneck. Herein, we exptl. and theor. investigate the temp.-dependent morphol. evolution of electrodeposited Li in solid electrolyte from -20°C to 90°C. It is found that as temp. increases, nuclei size, nuclei d., structural compactness and growth direction of electrodeposited Li are significantly altered. The thermal effect on Li nucleation and growth is further revealed by mol. dynamics simulations. Based on this, we propose a temp. modulation strategy to improve the cyclability of Li metal electrode. By charging at the optimal temp., the cycling life and Coulomb efficiency of Li electrode can be simultaneously improved. Our investigation gives valuable insights towards temp.-dependent Li deposition in solid electrolyte and will provide rational guidance for the thermal management of ASSLMBs operated in extreme environments.
- 32Wang, X.; Pawar, G.; Li, Y.; Ren, X.; Zhang, M.; Lu, B.; Banerjee, A.; Liu, P.; Dufek, E. J.; Zhang, J.-G.; Xiao, J.; Liu, J.; Meng, Y. S.; Liaw, B. Glassy Li metal anode for high-performance rechargeable Li batteries. Nat. Mater. 2020, 19, 1339– 1345, DOI: 10.1038/s41563-020-0729-132https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB3cXhsVGqtLjP&md5=86b6b978859de0b331c1073ca390c13bGlassy Li metal anode for high-performance rechargeable Li batteriesWang, Xuefeng; Pawar, Gorakh; Li, Yejing; Ren, Xiaodi; Zhang, Minghao; Lu, Bingyu; Banerjee, Abhik; Liu, Ping; Dufek, Eric J.; Zhang, Ji-Guang; Xiao, Jie; Liu, Jun; Meng, Ying Shirley; Liaw, BoryannNature Materials (2020), 19 (12), 1339-1345CODEN: NMAACR; ISSN:1476-1122. (Nature Research)Lithium metal has been considered an ideal anode for high-energy rechargeable Li batteries, although its nucleation and growth process remains mysterious, esp. at the nanoscale. Here, cryogenic transmission electron microscopy was used to reveal the evolving nanostructure of Li metal deposits at various transient states in the nucleation and growth process, in which a disorder-order phase transition was obsd. as a function of c.d. and deposition time. The at. interaction over wide spatial and temporal scales was depicted by reactive mol. dynamics simulations to assist in understanding the kinetics. Compared to cryst. Li, glassy Li outperforms in electrochem. reversibility, and it has a desired structure for high-energy rechargeable Li batteries. Our findings correlate the crystallinity of the nuclei with the subsequent growth of the nanostructure and morphol., and provide strategies to control and shape the mesostructure of Li metal to achieve high performance in rechargeable Li batteries.
- 33Ostadhossein, A.; Cubuk, E. D.; Tritsaris, G. A.; Kaxiras, E.; Zhang, S.; van Duin, A. C. T. Stress effects on the initial lithiation of crystalline silicon nanowires: reactive molecular dynamics simulations using ReaxFF. Phys. Chem. Chem. Phys. 2015, 17, 3832– 3840, DOI: 10.1039/c4cp05198j33https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXitFegs7fL&md5=68fbf83ba3e8055458b6cbf9b232e765Stress effects on the initial lithiation of crystalline silicon nanowires: reactive molecular dynamics simulations using ReaxFFOstadhossein, Alireza; Cubuk, Ekin D.; Tritsaris, Georgios A.; Kaxiras, Efthimios; Zhang, Sulin; van Duin, Adri C. T.Physical Chemistry Chemical Physics (2015), 17 (5), 3832-3840CODEN: PPCPFQ; ISSN:1463-9076. (Royal Society of Chemistry)Silicon (Si) has been recognized as a promising anode material for the next-generation high-capacity lithium (Li)-ion batteries because of its high theor. energy d. Recent in situ transmission electron microscopy (TEM) revealed that the electrochem. lithiation of cryst. Si nanowires (c-SiNWs) proceeds by the migration of the interface between the lithiated Si (LixSi) shell and the pristine unlithiated core, accompanied by solid-state amorphization. The underlying at. mechanisms of Li insertion into c-Si remain poorly understood. Herein, we perform mol. dynamics (MD) simulations using the reactive force field (ReaxFF) to characterize the lithiation process of c-SiNWs. Our calcns. show that ReaxFF can accurately reproduce the energy barriers of Li migration from DFT calcns. in both cryst. (c-Si) and amorphous Si (a-Si). The ReaxFF-based MD simulations reveal that Li insertion into interlayer spacing between two adjacent (111) planes results in the peeling-off of the (111) facets and subsequent amorphization, in agreement with exptl. observations. We find that breaking of the Si-Si bonds between (111)-bilayers requires a rather high local Li concn., which explains the atomically sharp amorphous-cryst. interface (ACI). Our stress anal. shows that lithiation induces compressive stress at the ACI layer, causing retardation or even the stagnation of the reaction front, also in good agreement with TEM observations. Lithiation at high temps. (e.g. 1200 K) shows that Li insertion into c-SiNW results in an amorphous to cryst. phase transformation at Li:Si compn. of ∼4.2 : 1. Our modeling results provide a comprehensive picture of the effects of reaction and diffusion-induced stress on the interfacial dynamics and mech. degrdn. of SiNW anodes under chemo-mech. lithiation.
- 34Tran, R.; Xu, Z.; Radhakrishnan, B.; Winston, D.; Sun, W.; Persson, K. A.; Ong, S. P. Surface energies of elemental crystals. Sci. Data 2016, 3, 160080, DOI: 10.1038/sdata.2016.8034https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XhsFCmsLzO&md5=146ff999161305aec429d5d682d40225Surface energies of elemental crystalsTran, Richard; Xu, Zihan; Radhakrishnan, Balachandran; Winston, Donald; Sun, Wenhao; Persson, Kristin A.; Ong, Shyue PingScientific Data (2016), 3 (), 160080CODEN: SDCABS; ISSN:2052-4463. (Nature Publishing Group)The surface energy is a fundamental property of the different facets of a crystal that is crucial to the understanding of various phenomena like surface segregation, roughening, catalytic activity, and the crystal's equil. shape. Such surface phenomena are esp. important at the nanoscale, where the large surface area to vol. ratios lead to properties that are significantly different from the bulk. In this work, we present the largest database of calcd. surface energies for elemental crystals to date. This database contains the surface energies of more than 100 polymorphs of about 70 elements, up to a max. Miller index of two and three for non-cubic and cubic crystals, resp. Well-known reconstruction schemes are also accounted for. The database is systematically improvable and has been rigorously validated against previous exptl. and computational data where available. We will describe the methodolgy used in constructing the database, and how it can be accessed for further studies and design of materials.
- 35Jain, A.; Ong, S. P.; Hautier, G.; Chen, W.; Richards, W. D.; Dacek, S.; Cholia, S.; Gunter, D.; Skinner, D.; Ceder, G.; Persson, K. A. Commentary: The materials project: A materials genome approach to accelerating materials innovation. APL Mater. 2013, 1, 011002, DOI: 10.1063/1.481232335https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC3sXhtlyktLjF&md5=88cb8642abed05e6b34a2191519b3ff3Commentary: The Materials Project: A materials genome approach to accelerating materials innovationJain, 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.
- 36Sholl, D. S. Density functional theory a practical introduction; Wiley: Hoboken, N.J, 2009.There is no corresponding record for this reference.
- 37Xu, 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.161573311437https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC28XitFWls7fM&md5=65404fc79b4b340710a0a4c287094cf0Enhanced strength and temperature dependence of mechanical properties of Li at small scales and its implications for Li metal anodesXu, 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.
- 38Owen, E. A.; Williams, G. I. X-ray measurements on lithium at low temperatures. Proc. Phys. Soc. Sect. A 1954, 67, 895– 900, DOI: 10.1088/0370-1298/67/10/306There is no corresponding record for this reference.
- 39C Nash, H.; Smith, C. S. Single-crystal elastic constants of lithium. J. Phys. Chem. Solids 1959, 9, 113– 118, DOI: 10.1016/0022-3697(59)90201-xThere is no corresponding record for this reference.
- 40Trivisonno, J.; Smith, C. S. Elastic constants of lithium-magnesium alloys. Acta Metall 1961, 9, 1064– 1071, DOI: 10.1016/0001-6160(61)90175-440https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaF38XjslCqsg%253D%253D&md5=e12b8b19b5c8f51c1010d5e603d25896Elastic constants of lithium-magnesium alloysTrivisonno, J.; Smith, Charles S.Acta Metallurgica (1961), 9 (), 1064-71CODEN: AMETAR; ISSN:0001-6160.The single-crystal elastic constants of dil. Li-Mg alloys were measured by using the ultrasonic pulse-echo technique. In terms of dln C÷dx, all fundamental elastic consts. increase with compn. by the following amts.: C44, 1.22; C', 1.03; B8, 1.20 per atom fraction. Small corrections for lattice parameter change upon alloying were made for C44 and C' by using the pressure derivatives of the elastic consts. of pure Li and the known variation of the lattice constant with compn. The remaining effect is ascribed to alloying alone. To understand both the elastic shear consts. of Li and their dependence on compn., one must include small neg. stiffness contributions arising in the Fermi energy, in addn. to the major and usual electrostatic stiffness. It is then possible to deduce the variation with compn. of each contribution from the measured total variation for the 2 shear consts., with the result that dln CE÷dx = 1.24 and dln CF÷dx 1.76.
- 41Slotwinski, T.; Trivisonno, J. Temperature dependence of the elastic constants of single crystal lithium. J. Phys. Chem. Solids 1969, 30, 1276– 1278, DOI: 10.1016/0022-3697(69)90386-241https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaF1MXkt1Ggu7o%253D&md5=6b7a03ef66c7374ba5f05a48fdeee992Temperature dependence of the elastic constants of single crystal lithiumSlotwinski, T.; Trivisonno, J.Journal of Physics and Chemistry of Solids (1969), 30 (5), 1276-8CODEN: JPCSAW; ISSN:0022-3697.A direct measurement was made of the temp. dependence of the elastic const. of single-crystal Li at 78-190°K. The crystals were oriented by a Laue transmission technique and acoustic specimens were prepd. with orientations along the [110] and [100] directions. The relative and abs. velocity measurements were made by using an ultrasonic pulse echo technique with a Mg buffer rod between the acoustic specimen and the transducer. The temp. was varied by a small resistance heater and measured with a thermocouple. Once these measurements were completed, the crystals were immersed in liq. N and the values of the elastic const. were detd. at 78°K. The method used to det. the elastic const. from the measured sound velocities in various crystallographic directions is that given by J. R. Neighbours and C. S. Smith (1950). The temp. dependence of the elastic const. was detd. by making an abs. measurement at 90°K. and measuring the change in transit time as a function of temp. between 90 and 200°K. The system used was capable of measuring a 1-nsec. change in 10 μsec., so that the temp. dependence measurements could be made with high precision. The plots of the elastic const. vs. temp. are remarkably linear. The quantities (dC/dT)p are quite different for all the elastic const. This is to be expected not only because of the increased accuracy in transit time measurements, but also because the detn. of the temp. dependence was inferred from independent measurements at 3 different temps. The adiabatic bulk modulus, Bs, was computed from the 3 directly measured elastic const. The isothermal bulk modulus, Bt, was computed from Bs. The obtained Bt and Bs values, derived from ultrasonic data, are in agreement with earlier results obtained from isothermal compressional data.
- 42Masias, A.; Felten, N.; Garcia-Mendez, R.; Wolfenstine, J.; Sakamoto, J. Elastic, plastic, and creep mechanical properties of lithium metal. J. Mater. Sci. 2019, 54, 2585– 2600, DOI: 10.1007/s10853-018-2971-342https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1cXhvVGnurnM&md5=598bc2a4384e9457a9833ffcd44d5755Elastic, plastic, and creep mechanical properties of lithium metalMasias, Alvaro; Felten, Nando; Garcia-Mendez, Regina; Wolfenstine, Jeff; Sakamoto, JeffJournal of Materials Science (2019), 54 (3), 2585-2600CODEN: JMTSAS; ISSN:0022-2461. (Springer)With the potential to dramatically increase energy d. compared to conventional lithium ion technol., lithium metal solid-state batteries (LMSSB) have attracted significant attention. However, little is known about the mech. properties of Li. The purpose of this study was to characterize the elastic and plastic mech. properties and creep behavior of Li. Elastic properties were measured using an acoustic technique (pulse-echo). The Young's modulus, shear modulus, and Poisson's ratio were detd. to be 7.82 GPa, 2.83 GPa, and 0.381, resp. To characterize the stress-strain behavior of Li in tension and compression, a unique load frame was used inside an inert atm. The yield strength was detd. to be between 0.73 and 0.81 MPa. The time-dependent deformation in tension was dramatically different compared to compression. In tension, power law creep was exhibited with a stress exponent of 6.56, suggesting that creep was controlled by dislocation climb. In compression, time-dependent deformation was characterized over a range of stress believed to be germane to LMSSB (0.8-2.4 MPa). At all compressive stresses, significant barreling and a decrease in strain rate with increasing time were obsd. The implications of this observation on the charge/discharge behavior of LMSSB will be discussed. We believe the anal. and mech. properties measured in this work will help in the design and development of LMSSB.
- 43Beg, M. M.; Nielsen, M. Temperature dependence of lattice dynamics of lithium 7. Phys. Rev. B 1976, 14, 4266– 4273, DOI: 10.1103/PhysRevB.14.426643https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaE2sXjs1Gmug%253D%253D&md5=bb70ba06c235e2ebe412e9ccf04d2941Temperature dependence of the lattice dynamics of lithium-7Beg, M. M.; Nielsen, M.Physical Review B: Solid State (1976), 14 (10), 4266-73CODEN: PLRBAQ; ISSN:0556-2805.Phonon dispersion relations in 7Li were measured by the coherent inelastic neutron scattering at 293 and 110 K. The frequency distributions were obtained from the exptl. data using the Born-von Karman general force model. The 1st-neighbor force consts. at 293 K are ∼10% smaller than those at 100 K. Temp. dependences of selected phonons were studied from 110 K to near the melting point. The energy shifts and phonon linewidths were evaluated at 293, 383, and 424 K by comparing the widths and energies to those measured at 110 K. The lattice parameter is 3.490 ± 0.003 Å at 110 K and 3.357 ± 0.003 Å at 424 K. The elastic consts. obtained at 293 K from the model parameters are (1011 dyne/cm2) C = 1.73 ± 0.10, C12 = 1.31 ± 0.20, and C44 = 0.84 ± 0.060. The temp. dependence of elastic const. was also detd.
- 44Wang, Y.; Dang, D.; Wang, M.; Xiao, X.; Cheng, Y.-T. Mechanical behavior of electroplated mossy lithium at room temperature studied by flat punch indentation. Appl. Phys. Lett. 2019, 115, 043903, DOI: 10.1063/1.511115044https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXhsVGrtLbN&md5=1863e241762fbcbee82c7f922b1a6628Mechanical behavior of electroplated mossy lithium at room temperature studied by flat punch indentationWang, Yikai; Dang, Dingying; Wang, Ming; Xiao, Xingcheng; Cheng, Yang-TseApplied Physics Letters (2019), 115 (4), 043903/1-043903/5CODEN: APPLAB; ISSN:0003-6951. (American Institute of Physics)We report the Young's modulus and deformation behavior of electroplated mossy Li at room temp. studied by flat punch indentation inside an Ar-filled glovebox. The Young's modulus of the mossy Li with a porosity of ∼62.3% is measured to be ∼2 GPa, which is smaller than that (∼7.8 GPa) of bulk Li. Both the mossy and bulk Li show clearly an indentation creep behavior. Despite its highly porous microstructure, the impression creep velocity of the mossy Li is less than one-thirtieth of that of bulk Li under the same stress. We propose possible mechanisms for the significantly higher deformation and creep resistance of the mossy Li over bulk Li. These findings are key to developing mech. suppression approaches to improve the cycling stability of Li metal electrodes. (c) 2019 American Institute of Physics.
- 45Zhang, H.; Li, C.; Djemia, P.; Yang, R.; Hu, Q. Prediction on temperature dependent elastic constants of “soft” metal Al by AIMD and QHA. J. Mater. Sci. Technol. 2020, 45, 92– 97, DOI: 10.1016/j.jmst.2019.11.02945https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BB38XjtFyrsb%252FL&md5=0957e564f3914dfefc6d91378f71da33Prediction on temperature dependent elastic constants of "soft" metal Al by AIMD and QHAZhang, Haijun; Li, Chenhui; Djemia, Philippe; Yang, Rui; Hu, QingmiaoJournal of Materials Science & Technology (Shenyang, China) (2020), 45 (), 92-97CODEN: JSCTEQ; ISSN:1005-0302. (Editorial Board of Journal of Materials Science & Technology)First-principles methods based on d. functional theory (DFT)are nowadays routinely applied to calc. the elastic consts. of materials at temp. of 0 K. Nevertheless, the first-principles calcns. of elastic consts. at finite temp. are not straightforward. In the present work, the feasibility of the ab initio mol. dynamic (AIMD)method in calcns. of the temp. dependent elastic consts. of relatively "soft" metals, taking fcc. (FCC) aluminum (Al) as example, is explored. The AIMD calcns. are performed with carefully selected strain tensors and strain magnitude. In parallel with the AIMD calcns., first-principles calcns. with the quasiharmonic approxn. (QHA) are performed as well. We show that all three independent elastic const. components (C11, C12 and C44) of Al from both the AIMD and QHA calcns. decrease with increasing temp. T, in good agreement with those from exptl. measurements. Our work allows us to quantify the individual contributions of the vol. expansion, lattice vibration (excluding those contributed to the vol. expansion), and electronic temp. effects to the temp. induced variation of the elastic consts. For Al with stable FCC crystal structure, the vol. expansion effect contributes the major part (about 75%∼80%) in the temp. induced variation of the elastic consts. The contribution of the lattice vibration is minor (about 20%∼25%) while the electronic temp. effect is negligible. Although the elastic consts. soften with increasing temp., FCC Al satisfies the Born elastic stability criteria with temp. up to the exptl. m.p.
- 46Nye, J. F. Physical properties of crystals: their representation by tensors and matrices; Clarendon Press: Oxford, 1985.There is no corresponding record for this reference.
- 47Jäckle, M.; Groß, A. Microscopic properties of lithium, sodium, and magnesium battery anode materials related to possible dendrite growth. J. Chem. Phys. 2014, 141, 174710, DOI: 10.1063/1.490105547https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC2M3mvFKqtg%253D%253D&md5=1a6b384f545454056968d9986c1d06bdMicroscopic properties of lithium, sodium, and magnesium battery anode materials related to possible dendrite growthJackle Markus; Gross AxelThe Journal of chemical physics (2014), 141 (17), 174710 ISSN:.Lithium and magnesium exhibit rather different properties as battery anode materials with respect to the phenomenon of dendrite formation which can lead to short-circuits in batteries. Diffusion processes are the key to understanding structure forming processes on surfaces. Therefore, we have determined adsorption energies and barriers for the self-diffusion on Li and Mg using periodic density functional theory calculations and contrasted the results to Na which is also regarded as a promising electrode material in batteries. According to our calculations, magnesium exhibits a tendency towards the growth of smooth surfaces as it exhibits lower diffusion barriers than lithium and sodium, and as an hcp metal it favors higher-coordinated configurations in contrast to the bcc metals Li and Na. These characteristic differences are expected to contribute to the unequal tendencies of these metals with respect to dendrite growth.
- 48Pande, V.; Viswanathan, V. Computational screening of current collectors for enabling anode-free lithium metal batteries. ACS Energy Lett. 2019, 4, 2952– 2959, DOI: 10.1021/acsenergylett.9b0230648https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC1MXitFaksL%252FM&md5=032bde91f3446ef848707f8783be4b8fComputational Screening of Current Collectors for Enabling Anode-Free Lithium Metal BatteriesPande, Vikram; Viswanathan, VenkatasubramanianACS Energy Letters (2019), 4 (12), 2952-2959CODEN: AELCCP; ISSN:2380-8195. (American Chemical Society)Li metal cells are key for achieving high specific energy for electrification of transportation and aviation. Anode-free cells are Li metal cells involving no excess Li with the highest possible specific energy. Anode-free cells are simpler, cheaper, and safer because they avoid the handling and manufg. of Li metal foils. The lack of excess Li magnifies issues related to dendrite growth and poor cycling in anode-free cells. The electrolyte and current collector surface play a crucial role in affecting anode-free cell cycling performance. The authors have computationally screened for candidate current collectors that nucleate Li effectively and allow uniform growth. These are detd. by the free energy of Li adsorption and Li surface diffusion barrier on candidate current collectors. Using d. functional theory calcns., Li alloys possess ideal characteristics for Li nucleation and growth. These can lead to vastly improved performance compared to current transition-metal current collectors.
- 49Henkelman, G.; Uberuaga, B. P.; Jónsson, H. A climbing image nudged elastic band method for finding saddle points and minimum energy paths. J. Chem. Phys. 2000, 113, 9901– 9904, DOI: 10.1063/1.132967249https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXosFagurc%253D&md5=3899b9e2e9e3eb74009987d96623f018A climbing image nudged elastic band method for finding saddle points and minimum energy pathsHenkelman, Graeme; Uberuaga, Blas P.; Jonsson, HannesJournal of Chemical Physics (2000), 113 (22), 9901-9904CODEN: JCPSA6; ISSN:0021-9606. (American Institute of Physics)A modification of the nudged elastic band method for finding min. energy paths is presented. One of the images is made to climb up along the elastic band to converge rigorously on the highest saddle point. Also, variable spring consts. are used to increase the d. of images near the top of the energy barrier to get an improved est. of the reaction coordinate near the saddle point. Applications to CH4 dissociative adsorption on Ir(111) and H2 on Si(100) using plane wave based d. functional theory are presented.
- 50Bell, R. P.; Hinshelwood, C. N. The theory of reactions involving proton transfers. Proc. R. Soc. Lond. Ser. A Math. Phys. Sci. 1936, 154, 414– 429, DOI: 10.1908/rspa.1936.0060There is no corresponding record for this reference.
- 51Evans, M. G.; Polanyi, M. Further considerations on the thermodynamics of chemical equilibria and reaction rates. Trans. Faraday Soc. 1936, 32, 1333– 1360, DOI: 10.1039/tf936320133351https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaA2sXisV2k&md5=8c2aa44e8a3764c445fc2d41d0c664bcFurther considerations of the thermodynamics of chemical equilibria and reaction ratesEvans, M. G.; Polanyi, M.Transactions of the Faraday Society (1936), 32 (), 1333-60CODEN: TFSOA4; ISSN:0014-7672.cf. C. A. 30, 3703.5. Theoretical discussion of the transition state, the effect of changes in mol. structure and of solvent, the relation between heats and entropies of soln., collision factors in soln., viscosity of the solvent, connection with the Nernst theorem and activation energies derived from energy surfaces.
- 52Schwoebel, R. L.; Shipsey, E. J. Step motion on crystal surfaces. J. Appl. Phys. 1966, 37, 3682– 3686, DOI: 10.1063/1.170790452https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADyaF28XkslOrs78%253D&md5=6e30ed5ad8c0c080aa8b28477612f4e1Step motion on crystal surfacesSchwoebel, Richard L.; Shipsey, Edward J.Journal of Applied Physics (1966), 37 (10), 3682-6CODEN: JAPIAU; ISSN:0021-8979.Steps on crystal surfaces capture atoms diffusing on the surface with certain probabilities and, in addn., the capture probability depends on the direction from which adsorbed atoms approach the step. A general solution for the time-dependent step distribution is obtained in terms of these probabilities and an arbitrary initial distribution of an infinite sequence of parallel steps. Coalescence of steps or stabilization of step spacings can occur as a consequence of assuming that capture probabilities are directionally dependent. Some of the implications of the theoretical model are related to the growth of real crystal surfaces.
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