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Ultranarrow-Band Wavelength-Selective Thermal Emission with Aperiodic Multilayered Metamaterials Designed by Bayesian Optimization

  • Atsushi Sakurai
    Atsushi Sakurai
    Department of Mechanical and Production Engineering, Niigata University, 8050 Ikarashi 2-no-cho, Niigata 950-2181, Japan
    National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, Japan
  • Kyohei Yada
    Kyohei Yada
    Graduate School of Science and Technology, Niigata University, 8050 Ikarashi 2-no-cho, Niigata 950-2181, Japan
    More by Kyohei Yada
  • Tetsushi Simomura
    Tetsushi Simomura
    Graduate School of Science and Technology, Niigata University, 8050 Ikarashi 2-no-cho, Niigata 950-2181, Japan
  • Shenghong Ju
    Shenghong Ju
    National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, Japan
    Department of Mechanical Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
    More by Shenghong Ju
  • Makoto Kashiwagi
    Makoto Kashiwagi
    Department of Mechanical Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
  • Hideyuki Okada
    Hideyuki Okada
    Graduate School of Science and Technology, Niigata University, 8050 Ikarashi 2-no-cho, Niigata 950-2181, Japan
  • Tadaaki Nagao
    Tadaaki Nagao
    National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, Japan
    Department of Condensed Matter Physics Graduate School of Science, Hokkaido University, Kita-10 Nishi-8, Kita-ku, Sapporo 060-0810, Japan
  • Koji Tsuda
    Koji Tsuda
    National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, Japan
    Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa 277-8561, Japan
    RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihombashi, Chuo-ku, Tokyo 103-0027, Japan
    More by Koji Tsuda
  • , and 
  • Junichiro Shiomi*
    Junichiro Shiomi
    National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, Japan
    Department of Mechanical Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
    RIKEN Center for Advanced Intelligence Project, 1-4-1 Nihombashi, Chuo-ku, Tokyo 103-0027, Japan
    *E-mail: [email protected]
Cite this: ACS Cent. Sci. 2019, 5, 2, 319–326
Publication Date (Web):January 22, 2019
https://doi.org/10.1021/acscentsci.8b00802

Copyright © 2019 American Chemical Society. This publication is licensed under these Terms of Use.

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Abstract

We computationally designed an ultranarrow-band wavelength-selective thermal radiator via a materials informatics method alternating between Bayesian optimization and thermal electromagnetic field calculation. For a given target infrared wavelength, the optimal structure was efficiently identified from over 8 billion candidates of multilayers consisting of multiple components (Si, Ge, and SiO2). The resulting optimized structure is an aperiodic multilayered metamaterial exhibiting high and sharp emissivity with a Q-factor of 273. The designed metamaterials were then fabricated, and reasonable experimental realization of the optimal performance was achieved with a Q-factor of 188, which is significantly higher than those of structures empirically designed and fabricated in the past. This is the first demonstration of the experimental realization of metamaterials designed by Bayesian optimization. The results facilitate the machine-learning-based design of metamaterials and advance our understanding of the narrow-band thermal emission mechanism of aperiodic multilayered metamaterials.

Synopsis

Ultranarrow-band wavelength-selective thermal radiators were designed by coupling electromagnetic calculation and Bayesian optimization, fabricated, and measured to exhibit top performance.

Introduction

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All materials emit or absorb thermal radiation. Therefore, in the exploration to utilize various thermal energy resources, tailoring thermal radiation plays a fundamentally important role. (1−3) While conventional thermal radiators typically exhibit broad-band, polarization-independent, and omnidirectional emission, the technology to control thermal radiation is rapidly progressing with the development of the fields of nanophotonics and metamaterials. Electromagnetic metamaterials are artificially engineered materials with characteristics tailored over a broad range of wavelengths. (4,5) Wavelength-selective narrow-band thermal emission control is a key technology with applications in high-efficiency thermophotovoltaics, (6−8) incandescent light sources, (9) biosensing, (10−12) microbolometers, (13,14) imaging, (15) and infrared heaters. (16) Different types of artificial nanostructures have been proposed in the past few decades: multilayer, (17,18) photonic crystal, (19−21) and metal–insulator–metal (MIM) metamaterials. (22−27)
Development of metamaterial thermal radiators generally requires high-cost nanofabrication. The reported narrow-band thermal radiator with the highest Q-factor to date (∼200) consists of 2D-grating-coupled surface phonon polaritons. (28) However, there is still a problem because there are large unwanted peaks and background in the emissivity spectra in the target wavelength range. This can be quantified by the low value of the figure of merit (defined to evaluate the radiator performance as will be shown later) due to the low wavelength selectivity. In addition, including another experimental demonstration of multiple quantum wells and a photonic crystal slab with a Q-factor of 107, (29) the complicated fabrication process faces practical problems because many applications of radiators require large surface area. In this sense, among various classes of metamaterials, multilayers with relatively less complication in fabrication have merit in scalability. Control of thermal emission by multilayer structures has been successfully demonstrated with a Fabry–Perot resonator with a Q-factor of 87 (30) and a distributed Bragg reflector with a Q-factor of 36. (31) However, these structures are usually realized by simple and periodic design, despite the fact that periodic structures are a tiny subset of the entire possible range of multilayer structures. Several studies have reported control of light by such “aperiodic” multilayer structures, (32−36) but these results were obtained by numerical simulation. Furthermore, the optimal design of aperiodic multilayered metamaterials with desired thermal emission characteristics has been difficult because the search space, i.e., the number of possible candidates, becomes enormous.
The key technology to overcome this challenge is “materials informatics” (MI), which has the capability to efficiently identify materials with preferred properties. MI aims to identify the “best” materials with optimal structure and/or composition using unrecognized complex correlations in the data. It has been applied to find novel materials such as cathode materials for the lithium-ion batteries, (37) nitride semiconductors composed of earth-abundant materials, (38) piezoelectric materials, (39) and thermoelectric materials. (40−44) While these works have aimed to realize high-throughput screening of the best materials from the pool of stoichiometric compounds, another course of MI aims to create nanostructures by identifying the optimal geometry that maximizes the objective properties. This includes nanoparticles embedded in a matrix to modulate heat conduction, (45) solid–solid interfaces to identify energetically stable structures, (46) and multicore structures of plasmonic nanowires to control optical scattering and cloaking effects. (47)
On the basis of the above progress in geometry optimization, the methodology using Bayesian optimization has been extended to the design of nanostructures with optimal thermal conductance (48) and thermoelectric figure of merit. (49) There, to efficiently identify the optimal structures among the enormous number of candidates, phonon/electron transport calculations and machine learning/prediction are alternately conducted. The previous works have shown that such an approach can considerably accelerate nanostructure design for transport properties. As the method is not limited to phonons/electrons and is applicable to any other quasi-particles, this work aims to perform such optimization for polaritons and associated thermal radiation. It should be noted that for thermal radiation there have been reports on the optimal design of multilayer structures using a genetic algorithm, (50,51) but genetic algorithms do not involve machine learning/prediction. In addition, recent studies (52,53) reported numerical nanophotonics designs based on neural networks. The essential drawback of their approach is that it is “exploitation-only”. There is plenty of evidence that the exploitation-only approach cannot be more efficient than the approach balancing exploitation and exploration. (54) On the other hand, Bayesian optimization identifies an unknown function with respect to the descriptors with as few iterations as possible, where, at every iteration, learning and prediction based on a Gaussian process are performed. Our approach uses Bayesian inference to quantify uncertainties and takes the optimal balance between exploration and exploitation, and we have used it to solve an essentially more difficult problem than the ones solved using neural networks. Although the previous studies optimized the thickness of each layer only, we optimized how the three materials are arranged, i.e., our method optimizes the ordering of the materials as well. There are a huge number of possible orderings, which adds substantial difficulty to the optimization problem.
In this work, we computationally designed an ultranarrow-band wavelength-selective thermal radiator via Bayesian optimization methods (55) and experimentally demonstrated the optical characteristics of the designed multilayered metamaterials. Potential applications of this study include infrared sensors, infrared imaging, and infrared heaters since the target wavelength is in the mid-infrared range.

Results and Discussion

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Figure 1a shows a schematic of the optimization method with MI combining electromagnetic simulation and Bayesian optimization. The designed metamaterial is divided into N unit layers with thickness dt. A unit layer can be either Ge, Si, or SiO2. The choice of compositions are commonly used semiconductor and dielectric materials for their high and low refractive indices, respectively. Since tungsten was chosen as the substrate, the substrate was considered opaque. Four basic elements are required when materials informatics is performed: the descriptor, calculator, evaluator, and optimization method. The descriptors are used to describe possible structure candidates during the optimization process. In this study, we used a text flag to indicate the state of each layer: “1”, “2”, and “3” represent the Ge, Si, and SiO2 layers, respectively. Such a simple descriptor has been shown to realize efficient optimization (48,49) in addition to being intuitive, general, and practical, which are important in the actual material development. As for the calculator, we employed the transfer matrix method (TMM) to calculate the emissivity spectra (see Methods).

Figure 1

Figure 1. (a) Schematic of the optimization method with material informatics combining electromagnetic simulation and Bayesian optimization. (b) Schematic of the ideal optical property of the narrow-band thermal radiator.

The desired optical property of the ultranarrow-band thermal radiator is shown in Figure 1b. The ideal radiator has a sharp and high thermal emission at a target wavelength λt with a bandwidth Δλ, and low thermal emission in the rest of the infrared wavelength region to reduce radiative heat loss. For the evaluator of designed multilayered metasurfaces, a figure of merit (FOM) is defined as follows:
(1)
where ελ is the spectral normal emissivity, I is the spectral blackbody intensity, and λmin and λmax are the minimum and maximum wavelengths considered for the optimization.
As we have N unit layers and three possible materials (Ge, Si, or SiO2), the total number of candidate structures is 3N, which becomes enormous for a useful range of N. For these large-scale problems, efficiency of optimization becomes critical, and thus, we need a method that surpasses conventional optimization tools. For this, we employ Bayesian optimization using the open-source library COMBO (see section S1 in the Supporting Information).
As shown in Figure 1a, suppose that FOMs of n candidates are initially calculated, and we are to select the next ones to calculate. A Bayesian regression function is learned from n pairs of descriptors and FOMs (i.e., training examples). For all of the remaining candidates, a predictive distribution of FOMs is estimated. Finally, FOMs are calculated for the selected candidates, and they are added to the training examples. By repetition of this procedure, the calculation of FOMs is scheduled optimally, and the optimized structure can be found quickly. Here, one problem is that the Bayesian optimization requires large computational memory because it uses information on the text data for all of the candidates. Therefore, we employed a hierarchical method to reduce the required size of computational memory, as will be explained later (also see Figure S1).
First, we computationally designed narrow-band thermal radiators with three candidate materials (Ge, Si, and SiO2) for a target wavelength λt of 6.0 μm. The wavelengths Δλ, λmin, and λmax were set to 4 nm, 4 μm, and 8 μm, respectively. The number of layers N was fixed at 18. Variation of the total thicknesses of the multilayers, ttotal, was also considered within the range from 3.6 to 4.0 μm with an increment of 0.02 μm, giving 21 variations of ttotal. Therefore, the total number of possible candidates is 318 × 21 = 8 135 830 269. It should be noted that it was not possible to account for structures with translational and reversal symmetries prior to the calculation to reduce the number of candidates. In this case, the numbers of initial and predicted candidate structures were set to 200 and 400, respectively. The computational load for this calculation was so large that all of the candidates could not be evaluated. For the sake of saving the computational memory, the optimization was pursued in hierarchical steps; the overall candidates were randomly divided into 42 000 groups, and the optimization was first performed for each group, after which the global best structure was identified by ranking these 42 000 local best structures. The total computational time was about 24 days on our cluster machine with 24 parallel computation (UNI-i9X, TOWA Electric, Inc.). The computational memory size in this work was about 128 GB, which set the maximum total number of layers to be 18. This could be enlarged by using a computer with a larger memory, but as the FOM of the designed structure is already close to unity, there is in fact not much room left for noticeable improvement even if we further increased the number of layers. Therefore, one can see the current setup to be nearly optimal and free from hardware restrictions.
The resulting optimized structures are shown in Figure 2a. It is interesting to note that the optimized structure with the maximum FOM consists of only Ge and SiO2 layers despite the fact that the optimization was performed including Si also. The obtained structure is a counterintuitive aperiodic multilayer, which is explicitly different from conventional multilayered thermal radiators with periodic structures. The total thickness ttotal of the optimal multilayer in this case is 3.80 μm.

Figure 2

Figure 2. (a) Optimized structure of the narrow-band thermal emitter with three material candidates (Ge, Si, and SiO2). The optimal structure turned out to consist of only Ge and SiO2 layers. (b) Histories of the FOMs of 20 randomly selected groups. The global-maximum FOM was found in a certain group that is indicated by the thick red line.

Figure 2b shows the history of the maximum FOM with respect to the number of calculated structures. Here we randomly chose the cases of 20 groups with about 200 000 candidates each to show the optimization efficiency and its statistics. The maximum FOM could be realized within calculations of 168 000 000 structures on average, which means only 2.06% of the candidate structures needed to be calculated to identify the optimal structure.
We also designed two other types of narrow-band thermal radiators with different target wavelengths of 5.0 and 7.0 μm. For these cases, using the finding in the case of λt = 6.0 μm that the optimal structure consists only of two species (Ge and SiO2), the optimization was performed for these two species instead of the above three species, which reduced the number of candidates to 218 × 21 = 5 505 024. The bandwidth Δλ (=4 nm) and the evaluation range of wavelengths (λmin = 4.0 μm and λmax = 8.0 μm) were kept the same as in the three-species optimization, and the number of initial candidate structures and predicted candidate structures were reduced to 100 and 20, respectively. The resulting optimized structures for λt = 5.0 and 7.0 μm (Figure 3a,b) consist of aperiodic multilayers similar to that for λt = 6.0 μm. The total thicknesses of the multilayers for the corresponding samples are ttotal = 3.78 and 3.96 μm, respectively.

Figure 3

Figure 3. Optimized structures of the narrow-band thermal emitters for the target wavelengths of (a) 5.0 and (b) 7.0 μm.

The computational load for the two-species calculations was relatively small, so all of the candidates could be calculated to validate the optimal structure and efficiency. As a result, the optimal structures obtained by Bayesian optimization were confirmed to be exactly the same as the structures with maximum FOM among all of the candidates. We also confirmed from the probability distributions (see Figure S2) that the probability monotonically decreases as the FOM value approaches the maximum without noticeable local minima, indicating that the current problem is suited for Bayesian optimization.
Figure 4a shows the corresponding calculated spectral directional emissivities of the optimized structures. Extremely sharp and high emissivity can be realized with the optimized structures, and there are no extra peaks within the wavelength range of interest (from 4 to 8 μm). The corresponding emissivities of the peaks are unity, and their Q-factors are 217, 273, and 233 for λt = 5, 6, and 7 μm, respectively.

Figure 4

Figure 4. (a) Calculated spectral directional emissivities of the optimized structures obtained with Bayesian optimization and (b) measured spectral directional emissivities of the fabricated structures aimed at λt = 5.0 μm (red), 6.0 μm (blue), and 7.0 μm (green). (c) Cross-sectional TEM images of the fabricated sample for λt = 6.0 μm.

Finally, we experimentally fabricated the optimized structures by sputtering to demonstrate the feasibility of the structural optimization. Figure 4b shows the measured spectral directional emissivities of the fabricated structures. The three sharp peaks that correspond to the ones seen in the numerical simulations can be clearly observed, although the locations of the peaks are shifted by about 0.5 μm relative to the designed structures. The obtained peak emissivity values of the λt = 5, 6, and 7 μm samples are 0.76, 0.83, and 0.61, and the Q-factors are 132, 188, and 109, respectively. The reason for the discrepancies in the peak positions and emissivities/Q-factors of the designed and fabricated structures could be that the thicknesses of the constituent layers in the fabricated samples somewhat deviate from the designed values. Table 1 quantifies the moderate but non-negligible differences between the layer thicknesses of the designed and fabricated structures obtained from the cross-sectional transmission electron microscopy (TEM) image for λt = 6.0 μm (Figure 4c) and the cross-sectional scanning electron microscopy (SEM) images for λt = 5.0 μm and λt = 7.0 μm (Figure S3). When we calculated the spectral directional emissivity for the layer thicknesses in the fabricated sample (Table 1), the position of the peak approached the experimentally measured value (Figure S4). The remaining discrepancy can be attributed to the minor differences in the optical properties of the sputtered material and those used as inputs to the numerical simulation, since the optical properties may differ depending on fabrication conditions such as the deposition rate.
Table 1. Layer Thicknesses of the Designed and Fabricated Structures (in μm)
 λt = 5.0 μmλt = 6.0 μmλt = 7.0 μm
layer no.sim.exp.sim.exp.sim.exp.
10.420.420.420.430.440.44
20.630.610.630.690.660.62
30.420.430.420.450.440.44
41.050.970.850.910.880.84
50.630.630.850.870.440.45
60.630.580.630.650.220.22
70.440.44
80.440.41
To determine the sharpness of the interface, the atomic concentrations at the Ge–SiO2 interface were observed by energy-dispersive X-ray spectroscopy (EDX) (Figures S5 and S6), and the interface was confirmed to be sharp with small interdiffusion. Although fabrication with a more accurately calibrated sputtering process would improve the reproduction of the designed performance, which remains to be our future task, the key features in the designed structure, namely, ultranarrow-band emission with controlled peak wavelength, were clearly realized in the experiments. The obtained Q-factors are about 217–273 in the computational design and about 109–188 in the experiment, which are significantly higher than the values reported in the previous studies. In addition, the FOM of this work is significantly higher than in previous experimental work: (28) the FOM of previous work, evaluated with the same wavelength range around the target wavelengths, was only 0.02 for 0° and further decreased to −0.16 for 1°, which are considerably smaller than the FOM of 0.77 for the current structure aimed at 6 μm. Although the locally extracted Q-factor in the previous work reached 200, (28) the emissivity spectra had much larger background and unwanted peaks, and thus, our experiment exhibits significantly higher wavelength selectivity. To our knowledge, this is the first demonstration that narrow-band thermal radiators designed by machine learning can be realized in experiments.
We now discuss the mechanism of the enhanced emission in terms of the magnetic field profiles shown in Figure 5. The intensities of the magnetic profiles were normalized by the intensity of the normal incident light. In Figure 5a,b for λt = 5 and 6 μm, there are strong confinements of electromagnetic energy in the Ge layer. On the other hand, in Figure 5c, for λt = 7 μm, strong confinement can be observed in the SiO2 layer. These emissivity enhancements originate from localized modes, similar to defect modes of photonic crystals. (56) Defect modes of photonic crystals exist inside a photonic band gap; therefore, this phenomenon is usually observed with periodic structures (see section S3). However, it is interesting to note that we observed a similar localized mode inside the aperiodic multilayered metamaterials. In other words, two or more optimized defect layers are introduced into the photonic crystals that effectively serve to constitute a sharp peak in the emissivity. In particular, in Figure 5c, the defect layer corresponds to three layers of a thin SiO2 layer and upper and lower Ge layers sandwiching the SiO2 layer. Therefore, the aperiodic structure, when optimized, successfully suppresses the unnecessary emissivity peaks due to higher-order harmonics, or in other words, shifts the peaks to a shorter wavelength range. To quantify how much power is absorbed by the proposed structure, the power dissipation density w was calculated as (57)
(2)
where ε0 is the permittivity of vacuum, ε″ is the imaginary part of the complex dielectric function, and ω is the angular frequency. The power dissipation densities, which are shown in Figure 5d–f, indicate the strong absorption at the tungsten substrate, although there is weak absorption within the SiO2 layer. Therefore, thermal energy dissipation mainly occurs in the metallic substrate because of the large optical loss.

Figure 5

Figure 5. (a–c) Contour plots of normalized magnetic field intensity and (d–f) power dissipation density for target wavelengths of (a, d) 5.0 μm, (b, e) 6.0 μm, and (c, f) 7.0 μm.

Because of the localized mode of the electromagnetic wave, the proposed emitter has an angular dependence of the optical properties (Figure S8). Isotropic thermal emission is preferred in certain applications such as infrared heaters. In this design, the angular dependences of the optical properties of transverse magnetic and transverse electric polarization within 20° are small, as the spectral shifts were only about 1%, which therefore is acceptable for practical applications. It should be noted that it is also possible to include the angular dependence in the FOM for preferred angular dependence, which will be explored in the future. The obtained results enhance our understanding of the narrow-band thermal emission mechanism of aperiodic multilayered metamaterials and facilitate the effective design of new metamaterials via Bayesian optimization.

Conclusion

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We computationally designed ultranarrow-band wavelength-selective thermal radiators via Bayesian optimization methods and experimentally demonstrated the optical characteristics of the designed multilayered metamaterials. The optimized structures could be found within calculations of only a few percent of the total numbers of candidate structures. The optimized structure for each target wavelength consists of aperiodic multilayers that give rise to sharp and near-unity emissivity. The designed structures were experimentally realized with reasonable accuracy, and the obtained structures exhibit Q-factors significantly larger than in previous works based on empirical design. Post-analysis of the magnetic fields of the structures revealed that the aperiodic multilayers can result in highly effective localization. The current work demonstrates the effectiveness, feasibility, and accuracy of developing narrow-band thermal emission materials using Bayesian optimization. In addition, the follow-up analysis of the mechanism demonstrates that such a materials informatics approach is also useful to enhance our understanding of narrow-band thermal emission.

Methods

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Safety Statement

No unexpected or unusually high safety hazards were encountered.

Electromagnetic Simulation

The TMM was used to solve Maxwell’s equations, allowing the calculation of the spectral radiative properties of multilayered metamaterials. (58) The spectral directional emissivity could be obtained by applying Kirchhoff’s law, i.e., ελ = 1 – Rλ, where Rλ is the reflectance obtained from the TMM simulation. The dielectric functions of SiO2, Si, Ge, and W were obtained from tabulated data. (59)

Bayesian Optimization

Bayesian optimization is a design algorithm based on machine learning (60) and a well-established technique for black-box optimization. (55) Bayesian prediction models are employed to predict the black-box function, where the uncertainty of the predicted function is also evaluated as predictive variance. The next candidate for the experiment is selected on the basis of predicted values and variances. Bayesian optimization has been recognized as an important technique in machine learning research because of successful hyperparameter tuning in deep learning algorithms. Bayesian optimization can be applied not only to materials sciences but also to various kinds of problems. However, the precondition is that each candidate point is represented as a numerical vector of identical dimensionality (i.e., descriptor).

Sample Fabrication and Reflectivity Measurement

The narrow-band thermal radiators designed on the basis of the Bayesian optimization method were experimentally fabricated and characterized. SiO2 and Ge layers were alternately deposited on a tungsten substrate by a magnetron sputtering machine. An FTIR spectrometer (iS50R, Thermo Scientific Nicolet) was used for reflectivity measurements, with an opaque gold film as a reference. In order to avoid atmospheric absorption, the measurements were conducted with flowing nitrogen gas. The incident angle was arranged within 1°, and therefore, the measured spectral reflectivity data could be considered as near normal reflectivity. Once the reflectivity was obtained, the spectral directional emissivity was obtained by applying Kirchhoff’s law.

Supporting Information

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The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acscentsci.8b00802.

  • Bayesian optimization, visualization and analysis of the nanostructure, and photonic band gap and localized mode (PDF)

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Author Information

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  • Corresponding Author
    • Junichiro Shiomi - National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, JapanDepartment of Mechanical Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, JapanRIKEN Center for Advanced Intelligence Project, 1-4-1 Nihombashi, Chuo-ku, Tokyo 103-0027, JapanOrcidhttp://orcid.org/0000-0002-3552-4555 Email: [email protected]
  • Authors
    • Atsushi Sakurai - Department of Mechanical and Production Engineering, Niigata University, 8050 Ikarashi 2-no-cho, Niigata 950-2181, JapanNational Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, Japan
    • Kyohei Yada - Graduate School of Science and Technology, Niigata University, 8050 Ikarashi 2-no-cho, Niigata 950-2181, Japan
    • Tetsushi Simomura - Graduate School of Science and Technology, Niigata University, 8050 Ikarashi 2-no-cho, Niigata 950-2181, Japan
    • Shenghong Ju - National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, JapanDepartment of Mechanical Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, JapanOrcidhttp://orcid.org/0000-0001-7863-6947
    • Makoto Kashiwagi - Department of Mechanical Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
    • Hideyuki Okada - Graduate School of Science and Technology, Niigata University, 8050 Ikarashi 2-no-cho, Niigata 950-2181, Japan
    • Tadaaki Nagao - National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, JapanDepartment of Condensed Matter Physics Graduate School of Science, Hokkaido University, Kita-10 Nishi-8, Kita-ku, Sapporo 060-0810, JapanOrcidhttp://orcid.org/0000-0002-6746-2686
    • Koji Tsuda - National Institute for Materials Science, 1-2-1 Sengen, Tsukuba 305-0047, JapanGraduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa 277-8561, JapanRIKEN Center for Advanced Intelligence Project, 1-4-1 Nihombashi, Chuo-ku, Tokyo 103-0027, JapanOrcidhttp://orcid.org/0000-0002-4288-1606
  • Notes
    The authors declare no competing financial interest.

Acknowledgments

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This work was supported in part by the Materials Research by Information Integration Initiative (MI2I) Project, the Center for Advanced Intelligence Project, RIKEN, KAKENHI (15K17985, 18K03974, and 16H04274) from JSPS, and CREST (JPMJCR13C3) from JST. We acknowledge Dr. Thang Duy Dao for his support for the reflectivity measurement and Dr. Bo Zhao for his valuable advice for electromagnetic simulation.

References

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This article references 60 other publications.

  1. 1
    Fan, S. Thermal photonics and energy applications. Joule 2017, 1 (2), 264273,  DOI: 10.1016/j.joule.2017.07.012
  2. 2
    Cui, L. J.; Jeong, W.; Fernandez-Hurtado, C.; Feist, J.; Garcia-Vidal, F. J.; Cuevas, J. C.; Meyhofer, E.; Reddy, P. Study of radiative heat transfer in Angstrom- and nanometre-sized gaps. Nat. Commun. 2017, 8, 14479,  DOI: 10.1038/ncomms14479
  3. 3
    Gluchko, S.; Palpant, B.; Volz, S.; Braive, R.; Antoni, T. Thermal excitation of broadband and long-range surface waves on SiO2 submicron films. Appl. Phys. Lett. 2017, 110 (26), 263108,  DOI: 10.1063/1.4989830
  4. 4
    Pendry, J. B.; Holden, A. J.; Robbins, D. J.; Stewart, W. J. Magnetism from conductors and enhanced nonlinear phenomena. IEEE Trans. Microwave Theory Tech. 1999, 47 (11), 20752084,  DOI: 10.1109/22.798002
  5. 5
    Smith, D. R.; Pendry, J. B.; Wiltshire, M. C. K. Metamaterials and negative refractive index. Science 2004, 305 (5685), 788792,  DOI: 10.1126/science.1096796
  6. 6
    De Zoysa, M.; Asano, T.; Mochizuki, K.; Oskooi, A.; Inoue, T.; Noda, S. Conversion of broadband to narrowband thermal emission through energy recycling. Nat. Photonics 2012, 6 (8), 535539,  DOI: 10.1038/nphoton.2012.146
  7. 7
    Bierman, D. M.; Lenert, A.; Chan, W. R.; Bhatia, B.; Celanovic, I.; Soljacic, M.; Wang, E. N. Enhanced photovoltaic energy conversion using thermally based spectral shaping. Nat. Energy 2016, 1, 16068,  DOI: 10.1038/nenergy.2016.68
  8. 8
    Zhou, Z.; Yehia, O.; Bermel, P. Integrated photonic crystal selective emitter for thermophotovoltaics. J. Nanophotonics 2016, 10, 016014  DOI: 10.1117/1.JNP.10.016014
  9. 9
    Ilic, O.; Bermel, P.; Chen, G.; Joannopoulos, J. D.; Celanovic, I.; Soljacic, M. Tailoring high-temperature radiation and the resurrection of the incandescent source. Nat. Nanotechnol. 2016, 11 (4), 320324,  DOI: 10.1038/nnano.2015.309
  10. 10
    Liu, N.; Mesch, M.; Weiss, T.; Hentschel, M.; Giessen, H. Infrared perfect absorber and its application as plasmonic sensor. Nano Lett. 2010, 10 (7), 23422348,  DOI: 10.1021/nl9041033
  11. 11
    Wu, C. H.; Khanikaev, A. B.; Adato, R.; Arju, N.; Yanik, A. A.; Altug, H.; Shvets, G. Fano-resonant asymmetric metamaterials for ultrasensitive spectroscopy and identification of molecular monolayers. Nat. Mater. 2012, 11 (1), 6975,  DOI: 10.1038/nmat3161
  12. 12
    Luo, S.; Zhao, J.; Zuo, D.; Wang, X. Perfect narrow band absorber for sensing applications. Opt. Express 2016, 24 (9), 92889294,  DOI: 10.1364/OE.24.009288
  13. 13
    Liu, X. L.; Wang, L. P.; Zhang, Z. M. Wideband tunable omnidirectional infrared absorbers based on doped-silicon nanowire arrays. J. Heat Transfer 2013, 135 (6), 061602  DOI: 10.1115/1.4023578
  14. 14
    Du, K.; Li, Q.; Zhang, W.; Yang, Y.; Qiu, M. Wavelength and thermal distribution selectable microbolometers based on metamaterial absorbers. IEEE Photonics J. 2015, 7 (3), 18,  DOI: 10.1109/JPHOT.2015.2406763
  15. 15
    Landy, N. I.; Bingham, C. M.; Tyler, T.; Jokerst, N.; Smith, D. R.; Padilla, W. J. Design, theory, and measurement of a polarization-insensitive absorber for terahertz imaging. Phys. Rev. B: Condens. Matter Mater. Phys. 2009, 79 (12), 125104,  DOI: 10.1103/PhysRevB.79.125104
  16. 16
    Totani, T.; Sakurai, A.; Kondo, Y. A wavelength control emitter for drying furnace. In Proceedings of the Asian Conference on Thermal Sciences 2017; KSME: Seoul, Korea, 2017; Paper ACTS-P00423.
  17. 17
    Bermel, P.; Ghebrebrhan, M.; Chan, W.; Yeng, Y. X.; Araghchini, M.; Hamam, R.; Marton, C. H.; Jensen, K. F.; Soljacic, M.; Joannopoulos, J. D.; Johnson, S. G.; Celanovic, I. Design and global optimization of high-efficiency thermophotovoltaic systems. Opt. Express 2010, 18 (19), A314A334,  DOI: 10.1364/OE.18.00A314
  18. 18
    Wang, H.; Alshehri, H.; Su, H.; Wang, L. Design, fabrication and optical characterizations of large-area lithography-free ultrathin multilayer selective solar coatings with excellent thermal stability in air. Sol. Energy Mater. Sol. Cells 2018, 174, 445452,  DOI: 10.1016/j.solmat.2017.09.025
  19. 19
    Nam, Y.; Yeng, Y. X.; Lenert, A.; Bermel, P.; Celanovic, I.; Soljacic, M.; Wang, E. N. Solar thermophotovoltaic energy conversion systems with two-dimensional tantalum photonic crystal absorbers and emitters. Sol. Energy Mater. Sol. Cells 2014, 122, 287296,  DOI: 10.1016/j.solmat.2013.12.012
  20. 20
    Rinnerbauer, V.; Lenert, A.; Bierman, D. M.; Yeng, Y. X.; Chan, W. R.; Geil, R. D.; Senkevich, J. J.; Joannopoulos, J. D.; Wang, E. N.; Soljacic, M.; Celanovic, I. Metallic photonic crystal absorber-emitter for efficient spectral control in high-temperature solar thermophotovoltaics. Adv. Energy Mater. 2014, 4 (12), 1400334,  DOI: 10.1002/aenm.201400334
  21. 21
    Yeng, Y. X.; Chou, J. B.; Rinnerbauer, V.; Shen, Y.; Kim, S.-G.; Joannopoulos, J. D.; Soljacic, M.; Celanovic, I. Global optimization of omnidirectional wavelength selective emitters/absorbers based on dielectric-filled anti-reflection coated two-dimensional metallic photonic crystals. Opt. Express 2014, 22 (18), 2171121718,  DOI: 10.1364/OE.22.021711
  22. 22
    Landy, N. I.; Sajuyigbe, S.; Mock, J. J.; Smith, D. R.; Padilla, W. J. Perfect metamaterial absorber. Phys. Rev. Lett. 2008, 100 (20), 207402,  DOI: 10.1103/PhysRevLett.100.207402
  23. 23
    Aydin, K.; Ferry, V. E.; Briggs, R. M.; Atwater, H. A. Broadband polarization-independent resonant light absorption using ultrathin plasmonic super absorbers. Nat. Commun. 2011, 2, 517,  DOI: 10.1038/ncomms1528
  24. 24
    Sakurai, A.; Zhao, B.; Zhang, Z. M. Resonant frequency and bandwidth of metamaterial emitters and absorbers predicted by an RLC circuit model. J. Quant. Spectrosc. Radiat. Transfer 2014, 149, 3340,  DOI: 10.1016/j.jqsrt.2014.07.024
  25. 25
    Sakurai, A.; Zhao, B.; Zhang, Z. M. Effect of polarization on dual-band infrared metamaterial emitters or absorbers. J. Quant. Spectrosc. Radiat. Transfer 2015, 158, 111118,  DOI: 10.1016/j.jqsrt.2014.11.018
  26. 26
    Dao, T. D.; Ishii, S.; Yokoyama, T.; Sawada, T.; Sugavaneshwar, R. P.; Chen, K.; Wada, Y.; Nabatame, T.; Nagao, T. Hole array perfect absorbers for spectrally selective mid-wavelength infrared pyroelectric detectors. ACS Photonics 2016, 3 (7), 12711278,  DOI: 10.1021/acsphotonics.6b00249
  27. 27
    Matsuno, Y.; Sakurai, A. Perfect infrared absorber and emitter based on a large-area metasurface. Opt. Mater. Express 2017, 7 (2), 618626,  DOI: 10.1364/OME.7.000618
  28. 28
    Dahan, N.; Niv, A.; Biener, G.; Gorodetski, Y.; Kleiner, V.; Hasman, E. Extraordinary coherent thermal emission from SiC due to coupled resonant cavities. J. Heat Transfer 2008, 130 (11), 112401,  DOI: 10.1115/1.2955475
  29. 29
    Inoue, T.; De Zoysa, M.; Asano, T.; Noda, S. Single-peak narrow-bandwidth mid-infrared thermal emitters based on quantum wells and photonic crystals. Appl. Phys. Lett. 2013, 102 (19), 191110,  DOI: 10.1063/1.4807174
  30. 30
    Zhao, D.; Meng, L.; Gong, H.; Chen, X.; Chen, Y.; Yan, M.; Li, Q.; Qiu, M. Ultra-narrow-band light dissipation by a stack of lamellar silver and alumina. Appl. Phys. Lett. 2014, 104 (22), 221107,  DOI: 10.1063/1.4881267
  31. 31
    Yang, Z.-Y.; Ishii, S.; Yokoyama, T.; Dao, T. D.; Sun, M.-G.; Pankin, P. S.; Timofeev, I. V.; Nagao, T.; Chen, K.-P. Narrowband wavelength selective thermal emitters by confined tamm plasmon polaritons. ACS Photonics 2017, 4 (9), 22122219,  DOI: 10.1021/acsphotonics.7b00408
  32. 32
    Granier, C. H.; Afzal, F. O.; Min, C.; Dowling, J. P.; Veronis, G. Optimized aperiodic highly directional narrowband infrared emitters. J. Opt. Soc. Am. B 2014, 31 (6), 13161321,  DOI: 10.1364/JOSAB.31.001316
  33. 33
    Sahel, S.; Amri, R.; Gamra, D.; Lejeune, M.; Benlahsen, M.; Zellama, K.; Bouchriha, H. Effect of sequence built on photonic band gap properties of one-dimensional quasi-periodic photonic crystals: application to thue-morse and double-period structures. Superlattices Microstruct. 2017, 111, 19,  DOI: 10.1016/j.spmi.2017.04.031
  34. 34
    Rephaeli, E.; Fan, S. Absorber and emitter for solar thermo-photovoltaic systems to achieve efficiency exceeding the Shockley-Queisser limit. Opt. Express 2009, 17 (17), 1514515159,  DOI: 10.1364/OE.17.015145
  35. 35
    Drevillon, J.; Ben-Abdallah, P. Ab initio design of coherent thermal sources. J. Appl. Phys. 2007, 102 (11), 114305,  DOI: 10.1063/1.2816244
  36. 36
    Sergeant, N. P.; Pincon, O.; Agrawal, M.; Peumans, P. Design of wide-angle solar-selective absorbers using aperiodic metal-dielectric stacks. Opt. Express 2009, 17 (25), 2280022812,  DOI: 10.1364/OE.17.022800
  37. 37
    Nishijima, M.; Ootani, T.; Kamimura, Y.; Sueki, T.; Esaki, S.; Murai, S.; Fujita, K.; Tanaka, K.; Ohira, K.; Koyama, Y.; Tanaka, I. Accelerated discovery of cathode materials with prolonged cycle life for lithium-ion battery. Nat. Commun. 2014, 5, 4553,  DOI: 10.1038/ncomms5553
  38. 38
    Hinuma, Y.; Hatakeyama, T.; Kumagai, Y.; Burton, L. A.; Sato, H.; Muraba, Y.; Iimura, S.; Hiramatsu, H.; Tanaka, I.; Hosono, H.; Oba, F. Discovery of earth-abundant nitride semiconductors by computational screening and high-pressure synthesis. Nat. Commun. 2016, 7, 11962,  DOI: 10.1038/ncomms11962
  39. 39
    Xue, D.; Balachandran, P. V.; Yuan, R.; Hu, T.; Qian, X.; Dougherty, E. R.; Lookman, T. Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning. Proc. Natl. Acad. Sci. U. S. A. 2016, 113 (47), 1330113306,  DOI: 10.1073/pnas.1607412113
  40. 40
    Carrete, J.; Li, W.; Mingo, N.; Wang, S.; Curtarolo, S. Finding unprecedentedly low-thermal-conductivity half-Heusler semiconductors via high-throughput materials modeling. Phys. Rev. X 2014, 4 (1), 011019  DOI: 10.1103/PhysRevX.4.011019
  41. 41
    Seko, A.; Togo, A.; Hayashi, H.; Tsuda, K.; Chaput, L.; Tanaka, I. Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization. Phys. Rev. Lett. 2015, 115 (20), 205901,  DOI: 10.1103/PhysRevLett.115.205901
  42. 42
    Oliynyk, A. O.; Antono, E.; Sparks, T. D.; Ghadbeigi, L.; Gaultois, M. W.; Meredig, B.; Mar, A. High-throughput machine-learning-driven synthesis of full-heusler compounds. Chem. Mater. 2016, 28 (20), 73247331,  DOI: 10.1021/acs.chemmater.6b02724
  43. 43
    van Roekeghem, A.; Carrete, J.; Oses, C.; Curtarolo, S.; Mingo, N. High-throughput computation of thermal conductivity of high-temperature solid phases: the case of oxide and fluoride perovskites. Phys. Rev. X 2016, 6 (4), 041061  DOI: 10.1103/PhysRevX.6.041061
  44. 44
    Gaultois, M. W.; Oliynyk, A. O.; Mar, A.; Sparks, T. D.; Mulholland, G. J.; Meredig, B. Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties. APL Mater. 2016, 4 (5), 053213  DOI: 10.1063/1.4952607
  45. 45
    Zhang, H.; Minnich, A. J. The best nanoparticle size distribution for minimum thermal conductivity. Sci. Rep. 2015, 5, 8995,  DOI: 10.1038/srep08995
  46. 46
    Kiyohara, S.; Oda, H.; Tsuda, K.; Mizoguchi, T. Acceleration of stable interface structure searching using a kriging approach. Jpn. J. Appl. Phys. 2016, 55 (4), 045502  DOI: 10.7567/JJAP.55.045502
  47. 47
    Mirzaei, A.; Miroshnichenko, A. E.; Shadrivov, I. V.; Kivshar, Y. S. Superscattering of light optimized by a genetic algorithm. Appl. Phys. Lett. 2014, 105 (1), 011109  DOI: 10.1063/1.4887475
  48. 48
    Ju, S.; Shiga, T.; Feng, L.; Hou, Z.; Tsuda, K.; Shiomi, J. Designing nanostructures for phonon transport via Bayesian optimization. Phys. Rev. X 2017, 7 (2), 021024  DOI: 10.1103/PhysRevX.7.021024
  49. 49
    Yamawaki, M.; Ohnishi, M.; Ju, S.; Shiomi, J. Multifunctional structural design of graphene thermoelectrics by Bayesian optimization. Sci. Adv. 2018, 4 (6), eaar4192  DOI: 10.1126/sciadv.aar4192
  50. 50
    Shimazaki, K.; Ohnishi, A.; Nagasaka, Y. Development of spectral selective multilayer film for a variable emittance device and its radiation properties measurements. Int. J. Thermophys. 2003, 24 (3), 757769,  DOI: 10.1023/A:1024044417708
  51. 51
    Sakurai, A.; Tanikawa, H.; Yamada, M. Computational design for a wide-angle cermet-based solar selective absorber for high temperature applications. J. Quant. Spectrosc. Radiat. Transfer 2014, 132, 8089,  DOI: 10.1016/j.jqsrt.2013.03.004
  52. 52
    Peurifoy, J.; Shen, Y.; Jing, L.; Yang, Y.; Cano-Renteria, F.; DeLacy, B. G.; Joannopoulos, J. D.; Tegmark, M.; Soljacic, M. Nanophotonic particle simulation and inverse design using artificial neural networks. Sci. Adv. 2018, 4 (6), eaar4206  DOI: 10.1126/sciadv.aar4206
  53. 53
    Liu, D.; Tan, Y.; Khoram, E.; Yu, Z. Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics 2018, 5 (4), 13651369,  DOI: 10.1021/acsphotonics.7b01377
  54. 54
    Shahriari, B.; Swersky, K.; Wang, Z.; Adams, R. P.; de Freitas, N. Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 2016, 104 (1), 148175,  DOI: 10.1109/JPROC.2015.2494218
  55. 55
    Ueno, T.; Rhone, T. D.; Hou, Z.; Mizoguchi, T.; Tsuda, K. COMBO: An efficient Bayesian optimization library for materials science. Materials Discovery 2016, 4, 1821,  DOI: 10.1016/j.md.2016.04.001
  56. 56
    Joannopoulos, J. D.; Villeneuve, P. R.; Fan, S. Photonic crystals: putting a new twist on light. Nature 1997, 386, 143,  DOI: 10.1038/386143a0
  57. 57
    Zhao, J. M.; Zhang, Z. M. Electromagnetic energy storage and power dissipation in nanostructures. J. Quant. Spectrosc. Radiat. Transfer 2015, 151, 4957,  DOI: 10.1016/j.jqsrt.2014.09.011
  58. 58
    Zhang, Z. M. Nano/Microscale Heat Transfer; McGraw-Hill: New York, 2007.
  59. 59
    Palik, E. D. Handbook of Optical Constants of Solids; Palik, E. D., Ed.; Academic Press: San Diego, CA, 1998; Vol. 3 .
  60. 60
    Dieb, T. M.; Tsuda, K. Machine Learning-Based Experimental Design in Materials Science. In Nanoinformatics; Tanaka, I., Ed.; Springer: Singapore, 2018; pp 6574.

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  39. Mingze He, Joshua Ryan Nolen, Josh Nordlander, Angela Cleri, Guanyu Lu, Thiago Arnaud, Nathaniel S. McIlwaine, Katja Diaz‐Granados, Eli Janzen, Thomas G. Folland, James H. Edgar, Jon‐Paul Maria, Joshua D. Caldwell. Coupled Tamm Phonon and Plasmon Polaritons for Designer Planar Multiresonance Absorbers. Advanced Materials 2023, 35 (20) https://doi.org/10.1002/adma.202209909
  40. Sterling G. Baird, Jason R. Hall, Taylor D. Sparks. Compactness matters: Improving Bayesian optimization efficiency of materials formulations through invariant search spaces. Computational Materials Science 2023, 224 , 112134. https://doi.org/10.1016/j.commatsci.2023.112134
  41. Maxime Giteau, Mitradeep Sarkar, Maria Paula Ayala, Michael T. Enders, Georgia T. Papadakis. Design Rules for Active Control of Narrowband Thermal Emission Using Phase-Change Materials. Physical Review Applied 2023, 19 (5) https://doi.org/10.1103/PhysRevApplied.19.L051002
  42. Yitao Sheng. Enhancement of a Graphene-Based Near-Field Thermophotovoltaic System by Optimization Algorithms and Dynamic Regulations. Photonics 2023, 10 (2) , 137. https://doi.org/10.3390/photonics10020137
  43. Zhe Yin, Lei Zhang, Jun Liu, Hongwei Gao, Liang Chen. Two-dimensional simple structured ultranarrow-band metamaterial perfect absorber with dielectric nanocylindrical array. Journal of Nanophotonics 2023, 17 (01) https://doi.org/10.1117/1.JNP.17.016002
  44. Zejia Liu, Zigui Zhang, Peifeng Xie, Zibo Miao. Design of Selective TPV Thermal Emitters Based on Bayesian Optimization Nesting Simulated Annealing. Energies 2023, 16 (1) , 416. https://doi.org/10.3390/en16010416
  45. Sterling G. Baird, Marianne Liu, Hasan M. Sayeed, Taylor D. Sparks. Data-driven materials discovery and synthesis using machine learning methods. 2023, 3-23. https://doi.org/10.1016/B978-0-12-823144-9.00079-0
  46. Daigo Furuya, Takuya Miyashita, Yoshio Miura, Yuma Iwasaki, Masato Kotsugi. Autonomous synthesis system integrating theoretical, informatics, and experimental approaches for large-magnetic-anisotropy materials. Science and Technology of Advanced Materials: Methods 2022, 2 (1) , 280-293. https://doi.org/10.1080/27660400.2022.2094698
  47. Zhijun Zhou, Biao Zhang, Cancheng Jiang, Haojin Wu. Design and theoretical study of a metamaterial absorber-emitter pair matched with a low-bandgap PV cell for an STPV system. Optical and Quantum Electronics 2022, 54 (12) https://doi.org/10.1007/s11082-022-04173-x
  48. WenBin Zhang, BoXiang Wang, JianMing Xu, ChangYing Zhao. High-quality quasi-monochromatic near-field radiative heat transfer designed by adaptive hybrid Bayesian optimization. Science China Technological Sciences 2022, 65 (12) , 2910-2920. https://doi.org/10.1007/s11431-022-2065-2
  49. Lin Deng, Yihao Xu, Yongmin Liu. Hybrid inverse design of photonic structures by combining optimization methods with neural networks. Photonics and Nanostructures - Fundamentals and Applications 2022, 52 , 101073. https://doi.org/10.1016/j.photonics.2022.101073
  50. Viktoriia Baibakova, Mahmoud Elzouka, Sean Lubner, Ravi Prasher, Anubhav Jain. Optical emissivity dataset of multi-material heterogeneous designs generated with automated figure extraction. Scientific Data 2022, 9 (1) https://doi.org/10.1038/s41597-022-01699-3
  51. Kenta Fukada, Michiko Seyama. Designing a multilayer film via machine learning of scientific literature. Scientific Reports 2022, 12 (1) https://doi.org/10.1038/s41598-022-05010-7
  52. Tianji Liu, Cheng Guo, Wei Li, Shanhui Fan. Thermal photonics with broken symmetries. eLight 2022, 2 (1) https://doi.org/10.1186/s43593-022-00025-z
  53. Xiang Huang, Shengluo Ma, Haidong Wang, Shangchao Lin, C.Y. Zhao, Hong Wang, Shenghong Ju. Enhancing thermoelectric properties of isotope graphene nanoribbons via machine learning guided manipulation of disordered antidots and interfaces. International Journal of Heat and Mass Transfer 2022, 197 , 123332. https://doi.org/10.1016/j.ijheatmasstransfer.2022.123332
  54. Hibiki Yoshida, Katsuyoshi Sakamoto, Naoya Miyashita, Koichi Yamaguchi, Qing Shen, Yoshitaka Okada, Tomah Sogabe. Ultrafast inverse design of quantum dot optical spectra via a joint TD-DFT learning scheme and deep reinforcement learning. AIP Advances 2022, 12 (11) https://doi.org/10.1063/5.0127546
  55. J. R. Capers, D. A. Patient, S. A. R. Horsley. Inverse design in the complex plane: Manipulating quasinormal modes. Physical Review A 2022, 106 (5) https://doi.org/10.1103/PhysRevA.106.053523
  56. Jiang Guo, Shenghong Ju, Yaerim Lee, A. Alperen Gunay, Junichiro Shiomi. Photonic design for color compatible radiative cooling accelerated by materials informatics. International Journal of Heat and Mass Transfer 2022, 195 , 123193. https://doi.org/10.1016/j.ijheatmasstransfer.2022.123193
  57. Xiu Liu, Lin Jing, Xiao Luo, Bowen Yu, Shen Du, Zexiao Wang, Hyeonggyun Kim, Yibai Zhong, Sheng Shen. Electrically driven thermal infrared metasurface with narrowband emission. Applied Physics Letters 2022, 121 (13) https://doi.org/10.1063/5.0116880
  58. Kazuma Hirasawa, Iona Nakami, Takumi Ooinoue, Tatsunori Asaoka, Garuda Fujii. Experimental demonstration of thermal cloaking metastructures designed by topology optimization. International Journal of Heat and Mass Transfer 2022, 194 , 123093. https://doi.org/10.1016/j.ijheatmasstransfer.2022.123093
  59. Haoxiang Zhang. Research on Multilayer Optimization based on Multi-Objective Simulation and Bayesian Optimization. 2022, 303-306. https://doi.org/10.1109/ICENIT57306.2022.00073
  60. Binze Ma, Yun Huang, Weiyi Zha, Bing Qin, Rui Qin, Pintu Ghosh, Sandeep Kaur, Min Qiu, Qiang Li. Narrowband diffuse thermal emitter based on surface phonon polaritons. Nanophotonics 2022, 11 (17) , 4115-4122. https://doi.org/10.1515/nanoph-2022-0047
  61. Shizheng Wen, Chunzhuo Dang, Xianglei Liu. A machine learning strategy for modeling and optimal design of near-field radiative heat transfer. Applied Physics Letters 2022, 121 (7) https://doi.org/10.1063/5.0103363
  62. Ken Araki, Richard Z. Zhang. An optimized self-adaptive thermal radiation turn-down coating with vanadium dioxide nanowire array. International Journal of Heat and Mass Transfer 2022, 191 , 122835. https://doi.org/10.1016/j.ijheatmasstransfer.2022.122835
  63. Sterling G. Baird, Marianne Liu, Taylor D. Sparks. High-dimensional Bayesian optimization of 23 hyperparameters over 100 iterations for an attention-based network to predict materials property: A case study on CrabNet using Ax platform and SAASBO. Computational Materials Science 2022, 211 , 111505. https://doi.org/10.1016/j.commatsci.2022.111505
  64. Donghe Chen, Fei Zhao, Kun Huang, Jiarui Feng, Jingtian Tang. Site Selection Planning of Urban Base Station. Highlights in Science, Engineering and Technology 2022, 4 , 236-242. https://doi.org/10.54097/hset.v4i.909
  65. Dezhao Zhu, Jiang Guo, Gang Yu, C. Y. Zhao, Hong Wang, Shenghong Ju. Designing thermal radiation metamaterials via a hybrid adversarial autoencoder and Bayesian optimization. Optics Letters 2022, 47 (14) , 3395. https://doi.org/10.1364/OL.453442
  66. P. Honarmandi, V. Attari, R. Arroyave. Accelerated materials design using batch Bayesian optimization: A case study for solving the inverse problem from materials microstructure to process specification. Computational Materials Science 2022, 210 , 111417. https://doi.org/10.1016/j.commatsci.2022.111417
  67. Zeyuan Ni, Hidefumi Matsui. Phase control of heterogeneous Hf x Zr (1−x) O 2 thin films by machine learning. Japanese Journal of Applied Physics 2022, 61 (SH) , SH1009. https://doi.org/10.35848/1347-4065/ac64e4
  68. Sterling G. Baird, Tran Q. Diep, Taylor D. Sparks. DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions. Digital Discovery 2022, 1 (3) , 226-240. https://doi.org/10.1039/D1DD00028D
  69. Kyohei Hanaoka. Comparison of conceptually different multi-objective Bayesian optimization methods for material design problems. Materials Today Communications 2022, 31 , 103440. https://doi.org/10.1016/j.mtcomm.2022.103440
  70. Han Wei, Hua Bao, Xiulin Ruan. Perspective: Predicting and optimizing thermal transport properties with machine learning methods. Energy and AI 2022, 8 , 100153. https://doi.org/10.1016/j.egyai.2022.100153
  71. Kazuma Isobe, Katsunori Hanamura. Resonance modes of a metal-semiconductor-metal multilayer mediated by electric charge. Journal of Physics Communications 2022, 6 (4) , 045006. https://doi.org/10.1088/2399-6528/ac678f
  72. Gerald Pühringer, Cristina Consani, Reyhaneh Jannesari, Clement Fleury, Florian Dubois, Jasmin Spettel, Thang Duy Dao, Gerald Stocker, Thomas Grille, Bernhard Jakoby. Design of a Slab Tamm Plasmon Resonator Coupled to a Multistrip Array Waveguide for the Mid Infrared. Sensors 2022, 22 (8) , 2968. https://doi.org/10.3390/s22082968
  73. Daiki Otaki, Hirofumi Nonaka, Noboru Yamada. Thermal design optimization of electronic circuit board layout with transient heating chips by using Bayesian optimization and thermal network model. International Journal of Heat and Mass Transfer 2022, 184 , 122263. https://doi.org/10.1016/j.ijheatmasstransfer.2021.122263
  74. Manaswin Oddiraju, Amir Behjat, Mostafa Nouh, Souma Chowdhury. Inverse Design Framework With Invertible Neural Networks for Passive Vibration Suppression in Phononic Structures. Journal of Mechanical Design 2022, 144 (2) https://doi.org/10.1115/1.4052300
  75. Tianzhe Huang, Qixiang Chen, Jinhua Huang, Yuehui Lu, Hua Xu, Meng Zhao, Yao Xu, Weijie Song. Scalable Colored Sub-Ambient Radiative Coolers Based on a Polymer-Tamm Photonic Structure. SSRN Electronic Journal 2022, 353 https://doi.org/10.2139/ssrn.4131645
  76. Eva De Leo, Ferry Prins, David J. Norris. Inverse design and realization of an optimized photonic multilayer for thermophotovoltaics. OSA Continuum 2021, 4 (12) , 3254. https://doi.org/10.1364/OSAC.434849
  77. Ken-ichi Uchida, Ryo Iguchi. Spintronic Thermal Management. Journal of the Physical Society of Japan 2021, 90 (12) https://doi.org/10.7566/JPSJ.90.122001
  78. Zhibin Ren, Ruyue Liu, Hongsheng Lu, Yue Guo, Rongbin Xie. Tunable ultranarrow-band metamaterial perfect absorber based on electromagnetically induced transparency structure. Optical Materials 2021, 122 , 111624. https://doi.org/10.1016/j.optmat.2021.111624
  79. Mingze He, J. Ryan Nolen, Josh Nordlander, Angela Cleri, Nathaniel S. McIlwaine, Yucheng Tang, Guanyu Lu, Thomas G. Folland, Bennett A. Landman, Jon-Paul Maria, Joshua D. Caldwell. Deterministic inverse design of Tamm plasmon thermal emitters with multi-resonant control. Nature Materials 2021, 20 (12) , 1663-1669. https://doi.org/10.1038/s41563-021-01094-0
  80. Dasen Zhang, Zhenzhen Liu, Guochao Wei, Zhaojun Hu, Jun-Jun Xiao, , , . Bayesian optimization of photonic nanojets generated by multilayer dielectric structures. 2021, 47. https://doi.org/10.1117/12.2604996
  81. Huanzheng Zhu, Ziquan Xu, Lu Cai, Han Wang, Hao Luo, Arnab Pattanayak, Pintu Ghosh, Min Qiu, Qiang Li. Ultrathin High Quality‐Factor Planar Absorbers/Emitters Based on Uniaxial/Biaxial Anisotropic van der Waals Polar Crystals. Advanced Optical Materials 2021, 9 (21) https://doi.org/10.1002/adom.202100645
  82. Shen Du, Ming-Jia Li, Ya-Ling He, Sheng Shen. Conceptual design of porous volumetric solar receiver using molten salt as heat transfer fluid. Applied Energy 2021, 301 , 117400. https://doi.org/10.1016/j.apenergy.2021.117400
  83. Zhibin Ren, Ruyue Liu, Yichao Zhang, Hongsheng Lu, Fengyi Li, Yuxin Liu, Xiaoling Hong, Yue Guo. Transmission reflection selective ultranarrow-band metamaterial filter based on electromagnetically induced transparency structure. Optics Communications 2021, 497 , 127159. https://doi.org/10.1016/j.optcom.2021.127159
  84. Hamzeh M. Jaradat. Ultra-thin single band metamaterial inspired absorber with suppressed higher order modes for terahertz applications. Optical Materials Express 2021, 11 (10) , 3341. https://doi.org/10.1364/OME.435817
  85. Kyohei Yada, Takashi Shimojo, Hideyuki Okada, Atsushi Sakurai. Theoretical and Numerical Analysis of Active Switching for Narrow-Band Thermal Emission with Graphene Ribbon Metasurface. Sensors 2021, 21 (20) , 6738. https://doi.org/10.3390/s21206738
  86. Olga Sarmanova, Kirill Laptinskiy, Sergey Burikov, Maria Khmeleva, Anna Fedyanina, Alexandra Tomskaya, Aleksandr Efitorov, Sergey Dolenko, Tatiana Dolenko. Machine learning algorithms to control concentrations of carbon nanocomplexes in a biological medium via optical absorption spectroscopy: how to choose and what to expect?. Applied Optics 2021, 60 (27) , 8291. https://doi.org/10.1364/AO.434984
  87. Xing-Yu Ma, Hou-Yi Lyu, Kuan-Rong Hao, Zhen-Gang Zhu, Qing-Bo Yan, Gang Su. High-efficient ab initio Bayesian active learning method and applications in prediction of two-dimensional functional materials. Nanoscale 2021, 13 (35) , 14694-14704. https://doi.org/10.1039/D1NR03886A
  88. Omar Khatib, Simiao Ren, Jordan Malof, Willie J. Padilla. Deep Learning the Electromagnetic Properties of Metamaterials—A Comprehensive Review. Advanced Functional Materials 2021, 31 (31) https://doi.org/10.1002/adfm.202101748
  89. Yulou Ouyang, Cuiqian Yu, Gang Yan, Jie Chen. Machine learning approach for the prediction and optimization of thermal transport properties. Frontiers of Physics 2021, 16 (4) https://doi.org/10.1007/s11467-020-1041-x
  90. Kyohei Hanaoka. Bayesian optimization for goal-oriented multi-objective inverse material design. iScience 2021, 24 (7) , 102781. https://doi.org/10.1016/j.isci.2021.102781
  91. Run Hu, Wang Xi, Yida Liu, Kechao Tang, Jinlin Song, Xiaobing Luo, Junqiao Wu, Cheng-Wei Qiu. Thermal camouflaging metamaterials. Materials Today 2021, 45 , 120-141. https://doi.org/10.1016/j.mattod.2020.11.013
  92. Chao Dong, Ke-Sheng Shen, Yun Zheng, Hong-Chao Liu, Jun Zhang, Shi-Qiang Xia, Feng Wu, Hai Lu, Xian-Zhou Zhang, Yu-Fang Liu. Quasiperiodic metamaterials empowered non-metallic broadband optical absorbers. Optics Express 2021, 29 (9) , 13576. https://doi.org/10.1364/OE.423353
  93. Takeshi Fujisawa, Kunimasa Saitoh. Bayesian direct-binary-search algorithm for the efficient design of mosaic-based power splitters. OSA Continuum 2021, 4 (4) , 1258. https://doi.org/10.1364/OSAC.422116
  94. Chufan Zhou, Zhiyu Wang, Ya-Lun Ho, Junichiro Shiomi, Jean-Jacques Delaunay. Optimized Tamm-plasmon structure by Differential Evolution algorithm for single and dual peaks hot-electron photodetection. Optical Materials 2021, 113 , 110857. https://doi.org/10.1016/j.optmat.2021.110857
  95. Ryo Yamawaki, Akiyo Tei, Kengo Ito, Jun Kikuchi. Decomposition Factor Analysis Based on Virtual Experiments throughout Bayesian Optimization for Compost-Degradable Polymers. Applied Sciences 2021, 11 (6) , 2820. https://doi.org/10.3390/app11062820
  96. Wang Xi, Yida Liu, Jinlin Song, Run Hu, Xiaobing Luo. High-throughput screening of a high-Q mid-infrared Tamm emitter by material informatics. Optics Letters 2021, 46 (4) , 888. https://doi.org/10.1364/OL.417378
  97. Run Hu, Junichiro Shiomi. Thermal Nanostructure Design by Materials Informatics. 2021, 153-195. https://doi.org/10.1007/978-3-030-68310-8_7
  98. Jiaxin Zhang, Sirui Bi, Guannan Zhang. A directional Gaussian smoothing optimization method for computational inverse design in nanophotonics. Materials & Design 2021, 197 , 109213. https://doi.org/10.1016/j.matdes.2020.109213
  99. Ryo Tamura, Yuki Takei, Shinichiro Imai, Maki Nakahara, Satoshi Shibata, Takashi Nakanishi, Masahiko Demura. Experimental design for the highly accurate prediction of material properties using descriptors obtained by measurement. Science and Technology of Advanced Materials: Methods 2021, 1 (1) , 152-161. https://doi.org/10.1080/27660400.2021.1963641
  100. Shenghong Ju, Shuntaro Shimizu, Junichiro Shiomi. Designing thermal functional materials by coupling thermal transport calculations and machine learning. Journal of Applied Physics 2020, 128 (16) https://doi.org/10.1063/5.0017042
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  • Abstract

    Figure 1

    Figure 1. (a) Schematic of the optimization method with material informatics combining electromagnetic simulation and Bayesian optimization. (b) Schematic of the ideal optical property of the narrow-band thermal radiator.

    Figure 2

    Figure 2. (a) Optimized structure of the narrow-band thermal emitter with three material candidates (Ge, Si, and SiO2). The optimal structure turned out to consist of only Ge and SiO2 layers. (b) Histories of the FOMs of 20 randomly selected groups. The global-maximum FOM was found in a certain group that is indicated by the thick red line.

    Figure 3

    Figure 3. Optimized structures of the narrow-band thermal emitters for the target wavelengths of (a) 5.0 and (b) 7.0 μm.

    Figure 4

    Figure 4. (a) Calculated spectral directional emissivities of the optimized structures obtained with Bayesian optimization and (b) measured spectral directional emissivities of the fabricated structures aimed at λt = 5.0 μm (red), 6.0 μm (blue), and 7.0 μm (green). (c) Cross-sectional TEM images of the fabricated sample for λt = 6.0 μm.

    Figure 5

    Figure 5. (a–c) Contour plots of normalized magnetic field intensity and (d–f) power dissipation density for target wavelengths of (a, d) 5.0 μm, (b, e) 6.0 μm, and (c, f) 7.0 μm.

  • References

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    This article references 60 other publications.

    1. 1
      Fan, S. Thermal photonics and energy applications. Joule 2017, 1 (2), 264273,  DOI: 10.1016/j.joule.2017.07.012
    2. 2
      Cui, L. J.; Jeong, W.; Fernandez-Hurtado, C.; Feist, J.; Garcia-Vidal, F. J.; Cuevas, J. C.; Meyhofer, E.; Reddy, P. Study of radiative heat transfer in Angstrom- and nanometre-sized gaps. Nat. Commun. 2017, 8, 14479,  DOI: 10.1038/ncomms14479
    3. 3
      Gluchko, S.; Palpant, B.; Volz, S.; Braive, R.; Antoni, T. Thermal excitation of broadband and long-range surface waves on SiO2 submicron films. Appl. Phys. Lett. 2017, 110 (26), 263108,  DOI: 10.1063/1.4989830
    4. 4
      Pendry, J. B.; Holden, A. J.; Robbins, D. J.; Stewart, W. J. Magnetism from conductors and enhanced nonlinear phenomena. IEEE Trans. Microwave Theory Tech. 1999, 47 (11), 20752084,  DOI: 10.1109/22.798002
    5. 5
      Smith, D. R.; Pendry, J. B.; Wiltshire, M. C. K. Metamaterials and negative refractive index. Science 2004, 305 (5685), 788792,  DOI: 10.1126/science.1096796
    6. 6
      De Zoysa, M.; Asano, T.; Mochizuki, K.; Oskooi, A.; Inoue, T.; Noda, S. Conversion of broadband to narrowband thermal emission through energy recycling. Nat. Photonics 2012, 6 (8), 535539,  DOI: 10.1038/nphoton.2012.146
    7. 7
      Bierman, D. M.; Lenert, A.; Chan, W. R.; Bhatia, B.; Celanovic, I.; Soljacic, M.; Wang, E. N. Enhanced photovoltaic energy conversion using thermally based spectral shaping. Nat. Energy 2016, 1, 16068,  DOI: 10.1038/nenergy.2016.68
    8. 8
      Zhou, Z.; Yehia, O.; Bermel, P. Integrated photonic crystal selective emitter for thermophotovoltaics. J. Nanophotonics 2016, 10, 016014  DOI: 10.1117/1.JNP.10.016014
    9. 9
      Ilic, O.; Bermel, P.; Chen, G.; Joannopoulos, J. D.; Celanovic, I.; Soljacic, M. Tailoring high-temperature radiation and the resurrection of the incandescent source. Nat. Nanotechnol. 2016, 11 (4), 320324,  DOI: 10.1038/nnano.2015.309
    10. 10
      Liu, N.; Mesch, M.; Weiss, T.; Hentschel, M.; Giessen, H. Infrared perfect absorber and its application as plasmonic sensor. Nano Lett. 2010, 10 (7), 23422348,  DOI: 10.1021/nl9041033
    11. 11
      Wu, C. H.; Khanikaev, A. B.; Adato, R.; Arju, N.; Yanik, A. A.; Altug, H.; Shvets, G. Fano-resonant asymmetric metamaterials for ultrasensitive spectroscopy and identification of molecular monolayers. Nat. Mater. 2012, 11 (1), 6975,  DOI: 10.1038/nmat3161
    12. 12
      Luo, S.; Zhao, J.; Zuo, D.; Wang, X. Perfect narrow band absorber for sensing applications. Opt. Express 2016, 24 (9), 92889294,  DOI: 10.1364/OE.24.009288
    13. 13
      Liu, X. L.; Wang, L. P.; Zhang, Z. M. Wideband tunable omnidirectional infrared absorbers based on doped-silicon nanowire arrays. J. Heat Transfer 2013, 135 (6), 061602  DOI: 10.1115/1.4023578
    14. 14
      Du, K.; Li, Q.; Zhang, W.; Yang, Y.; Qiu, M. Wavelength and thermal distribution selectable microbolometers based on metamaterial absorbers. IEEE Photonics J. 2015, 7 (3), 18,  DOI: 10.1109/JPHOT.2015.2406763
    15. 15
      Landy, N. I.; Bingham, C. M.; Tyler, T.; Jokerst, N.; Smith, D. R.; Padilla, W. J. Design, theory, and measurement of a polarization-insensitive absorber for terahertz imaging. Phys. Rev. B: Condens. Matter Mater. Phys. 2009, 79 (12), 125104,  DOI: 10.1103/PhysRevB.79.125104
    16. 16
      Totani, T.; Sakurai, A.; Kondo, Y. A wavelength control emitter for drying furnace. In Proceedings of the Asian Conference on Thermal Sciences 2017; KSME: Seoul, Korea, 2017; Paper ACTS-P00423.
    17. 17
      Bermel, P.; Ghebrebrhan, M.; Chan, W.; Yeng, Y. X.; Araghchini, M.; Hamam, R.; Marton, C. H.; Jensen, K. F.; Soljacic, M.; Joannopoulos, J. D.; Johnson, S. G.; Celanovic, I. Design and global optimization of high-efficiency thermophotovoltaic systems. Opt. Express 2010, 18 (19), A314A334,  DOI: 10.1364/OE.18.00A314
    18. 18
      Wang, H.; Alshehri, H.; Su, H.; Wang, L. Design, fabrication and optical characterizations of large-area lithography-free ultrathin multilayer selective solar coatings with excellent thermal stability in air. Sol. Energy Mater. Sol. Cells 2018, 174, 445452,  DOI: 10.1016/j.solmat.2017.09.025
    19. 19
      Nam, Y.; Yeng, Y. X.; Lenert, A.; Bermel, P.; Celanovic, I.; Soljacic, M.; Wang, E. N. Solar thermophotovoltaic energy conversion systems with two-dimensional tantalum photonic crystal absorbers and emitters. Sol. Energy Mater. Sol. Cells 2014, 122, 287296,  DOI: 10.1016/j.solmat.2013.12.012
    20. 20
      Rinnerbauer, V.; Lenert, A.; Bierman, D. M.; Yeng, Y. X.; Chan, W. R.; Geil, R. D.; Senkevich, J. J.; Joannopoulos, J. D.; Wang, E. N.; Soljacic, M.; Celanovic, I. Metallic photonic crystal absorber-emitter for efficient spectral control in high-temperature solar thermophotovoltaics. Adv. Energy Mater. 2014, 4 (12), 1400334,  DOI: 10.1002/aenm.201400334
    21. 21
      Yeng, Y. X.; Chou, J. B.; Rinnerbauer, V.; Shen, Y.; Kim, S.-G.; Joannopoulos, J. D.; Soljacic, M.; Celanovic, I. Global optimization of omnidirectional wavelength selective emitters/absorbers based on dielectric-filled anti-reflection coated two-dimensional metallic photonic crystals. Opt. Express 2014, 22 (18), 2171121718,  DOI: 10.1364/OE.22.021711
    22. 22
      Landy, N. I.; Sajuyigbe, S.; Mock, J. J.; Smith, D. R.; Padilla, W. J. Perfect metamaterial absorber. Phys. Rev. Lett. 2008, 100 (20), 207402,  DOI: 10.1103/PhysRevLett.100.207402
    23. 23
      Aydin, K.; Ferry, V. E.; Briggs, R. M.; Atwater, H. A. Broadband polarization-independent resonant light absorption using ultrathin plasmonic super absorbers. Nat. Commun. 2011, 2, 517,  DOI: 10.1038/ncomms1528
    24. 24
      Sakurai, A.; Zhao, B.; Zhang, Z. M. Resonant frequency and bandwidth of metamaterial emitters and absorbers predicted by an RLC circuit model. J. Quant. Spectrosc. Radiat. Transfer 2014, 149, 3340,  DOI: 10.1016/j.jqsrt.2014.07.024
    25. 25
      Sakurai, A.; Zhao, B.; Zhang, Z. M. Effect of polarization on dual-band infrared metamaterial emitters or absorbers. J. Quant. Spectrosc. Radiat. Transfer 2015, 158, 111118,  DOI: 10.1016/j.jqsrt.2014.11.018
    26. 26
      Dao, T. D.; Ishii, S.; Yokoyama, T.; Sawada, T.; Sugavaneshwar, R. P.; Chen, K.; Wada, Y.; Nabatame, T.; Nagao, T. Hole array perfect absorbers for spectrally selective mid-wavelength infrared pyroelectric detectors. ACS Photonics 2016, 3 (7), 12711278,  DOI: 10.1021/acsphotonics.6b00249
    27. 27
      Matsuno, Y.; Sakurai, A. Perfect infrared absorber and emitter based on a large-area metasurface. Opt. Mater. Express 2017, 7 (2), 618626,  DOI: 10.1364/OME.7.000618
    28. 28
      Dahan, N.; Niv, A.; Biener, G.; Gorodetski, Y.; Kleiner, V.; Hasman, E. Extraordinary coherent thermal emission from SiC due to coupled resonant cavities. J. Heat Transfer 2008, 130 (11), 112401,  DOI: 10.1115/1.2955475
    29. 29
      Inoue, T.; De Zoysa, M.; Asano, T.; Noda, S. Single-peak narrow-bandwidth mid-infrared thermal emitters based on quantum wells and photonic crystals. Appl. Phys. Lett. 2013, 102 (19), 191110,  DOI: 10.1063/1.4807174
    30. 30
      Zhao, D.; Meng, L.; Gong, H.; Chen, X.; Chen, Y.; Yan, M.; Li, Q.; Qiu, M. Ultra-narrow-band light dissipation by a stack of lamellar silver and alumina. Appl. Phys. Lett. 2014, 104 (22), 221107,  DOI: 10.1063/1.4881267
    31. 31
      Yang, Z.-Y.; Ishii, S.; Yokoyama, T.; Dao, T. D.; Sun, M.-G.; Pankin, P. S.; Timofeev, I. V.; Nagao, T.; Chen, K.-P. Narrowband wavelength selective thermal emitters by confined tamm plasmon polaritons. ACS Photonics 2017, 4 (9), 22122219,  DOI: 10.1021/acsphotonics.7b00408
    32. 32
      Granier, C. H.; Afzal, F. O.; Min, C.; Dowling, J. P.; Veronis, G. Optimized aperiodic highly directional narrowband infrared emitters. J. Opt. Soc. Am. B 2014, 31 (6), 13161321,  DOI: 10.1364/JOSAB.31.001316
    33. 33
      Sahel, S.; Amri, R.; Gamra, D.; Lejeune, M.; Benlahsen, M.; Zellama, K.; Bouchriha, H. Effect of sequence built on photonic band gap properties of one-dimensional quasi-periodic photonic crystals: application to thue-morse and double-period structures. Superlattices Microstruct. 2017, 111, 19,  DOI: 10.1016/j.spmi.2017.04.031
    34. 34
      Rephaeli, E.; Fan, S. Absorber and emitter for solar thermo-photovoltaic systems to achieve efficiency exceeding the Shockley-Queisser limit. Opt. Express 2009, 17 (17), 1514515159,  DOI: 10.1364/OE.17.015145
    35. 35
      Drevillon, J.; Ben-Abdallah, P. Ab initio design of coherent thermal sources. J. Appl. Phys. 2007, 102 (11), 114305,  DOI: 10.1063/1.2816244
    36. 36
      Sergeant, N. P.; Pincon, O.; Agrawal, M.; Peumans, P. Design of wide-angle solar-selective absorbers using aperiodic metal-dielectric stacks. Opt. Express 2009, 17 (25), 2280022812,  DOI: 10.1364/OE.17.022800
    37. 37
      Nishijima, M.; Ootani, T.; Kamimura, Y.; Sueki, T.; Esaki, S.; Murai, S.; Fujita, K.; Tanaka, K.; Ohira, K.; Koyama, Y.; Tanaka, I. Accelerated discovery of cathode materials with prolonged cycle life for lithium-ion battery. Nat. Commun. 2014, 5, 4553,  DOI: 10.1038/ncomms5553
    38. 38
      Hinuma, Y.; Hatakeyama, T.; Kumagai, Y.; Burton, L. A.; Sato, H.; Muraba, Y.; Iimura, S.; Hiramatsu, H.; Tanaka, I.; Hosono, H.; Oba, F. Discovery of earth-abundant nitride semiconductors by computational screening and high-pressure synthesis. Nat. Commun. 2016, 7, 11962,  DOI: 10.1038/ncomms11962
    39. 39
      Xue, D.; Balachandran, P. V.; Yuan, R.; Hu, T.; Qian, X.; Dougherty, E. R.; Lookman, T. Accelerated search for BaTiO3-based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning. Proc. Natl. Acad. Sci. U. S. A. 2016, 113 (47), 1330113306,  DOI: 10.1073/pnas.1607412113
    40. 40
      Carrete, J.; Li, W.; Mingo, N.; Wang, S.; Curtarolo, S. Finding unprecedentedly low-thermal-conductivity half-Heusler semiconductors via high-throughput materials modeling. Phys. Rev. X 2014, 4 (1), 011019  DOI: 10.1103/PhysRevX.4.011019
    41. 41
      Seko, A.; Togo, A.; Hayashi, H.; Tsuda, K.; Chaput, L.; Tanaka, I. Prediction of low-thermal-conductivity compounds with first-principles anharmonic lattice-dynamics calculations and Bayesian optimization. Phys. Rev. Lett. 2015, 115 (20), 205901,  DOI: 10.1103/PhysRevLett.115.205901
    42. 42
      Oliynyk, A. O.; Antono, E.; Sparks, T. D.; Ghadbeigi, L.; Gaultois, M. W.; Meredig, B.; Mar, A. High-throughput machine-learning-driven synthesis of full-heusler compounds. Chem. Mater. 2016, 28 (20), 73247331,  DOI: 10.1021/acs.chemmater.6b02724
    43. 43
      van Roekeghem, A.; Carrete, J.; Oses, C.; Curtarolo, S.; Mingo, N. High-throughput computation of thermal conductivity of high-temperature solid phases: the case of oxide and fluoride perovskites. Phys. Rev. X 2016, 6 (4), 041061  DOI: 10.1103/PhysRevX.6.041061
    44. 44
      Gaultois, M. W.; Oliynyk, A. O.; Mar, A.; Sparks, T. D.; Mulholland, G. J.; Meredig, B. Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties. APL Mater. 2016, 4 (5), 053213  DOI: 10.1063/1.4952607
    45. 45
      Zhang, H.; Minnich, A. J. The best nanoparticle size distribution for minimum thermal conductivity. Sci. Rep. 2015, 5, 8995,  DOI: 10.1038/srep08995
    46. 46
      Kiyohara, S.; Oda, H.; Tsuda, K.; Mizoguchi, T. Acceleration of stable interface structure searching using a kriging approach. Jpn. J. Appl. Phys. 2016, 55 (4), 045502  DOI: 10.7567/JJAP.55.045502
    47. 47
      Mirzaei, A.; Miroshnichenko, A. E.; Shadrivov, I. V.; Kivshar, Y. S. Superscattering of light optimized by a genetic algorithm. Appl. Phys. Lett. 2014, 105 (1), 011109  DOI: 10.1063/1.4887475
    48. 48
      Ju, S.; Shiga, T.; Feng, L.; Hou, Z.; Tsuda, K.; Shiomi, J. Designing nanostructures for phonon transport via Bayesian optimization. Phys. Rev. X 2017, 7 (2), 021024  DOI: 10.1103/PhysRevX.7.021024
    49. 49
      Yamawaki, M.; Ohnishi, M.; Ju, S.; Shiomi, J. Multifunctional structural design of graphene thermoelectrics by Bayesian optimization. Sci. Adv. 2018, 4 (6), eaar4192  DOI: 10.1126/sciadv.aar4192
    50. 50
      Shimazaki, K.; Ohnishi, A.; Nagasaka, Y. Development of spectral selective multilayer film for a variable emittance device and its radiation properties measurements. Int. J. Thermophys. 2003, 24 (3), 757769,  DOI: 10.1023/A:1024044417708
    51. 51
      Sakurai, A.; Tanikawa, H.; Yamada, M. Computational design for a wide-angle cermet-based solar selective absorber for high temperature applications. J. Quant. Spectrosc. Radiat. Transfer 2014, 132, 8089,  DOI: 10.1016/j.jqsrt.2013.03.004
    52. 52
      Peurifoy, J.; Shen, Y.; Jing, L.; Yang, Y.; Cano-Renteria, F.; DeLacy, B. G.; Joannopoulos, J. D.; Tegmark, M.; Soljacic, M. Nanophotonic particle simulation and inverse design using artificial neural networks. Sci. Adv. 2018, 4 (6), eaar4206  DOI: 10.1126/sciadv.aar4206
    53. 53
      Liu, D.; Tan, Y.; Khoram, E.; Yu, Z. Training deep neural networks for the inverse design of nanophotonic structures. ACS Photonics 2018, 5 (4), 13651369,  DOI: 10.1021/acsphotonics.7b01377
    54. 54
      Shahriari, B.; Swersky, K.; Wang, Z.; Adams, R. P.; de Freitas, N. Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 2016, 104 (1), 148175,  DOI: 10.1109/JPROC.2015.2494218
    55. 55
      Ueno, T.; Rhone, T. D.; Hou, Z.; Mizoguchi, T.; Tsuda, K. COMBO: An efficient Bayesian optimization library for materials science. Materials Discovery 2016, 4, 1821,  DOI: 10.1016/j.md.2016.04.001
    56. 56
      Joannopoulos, J. D.; Villeneuve, P. R.; Fan, S. Photonic crystals: putting a new twist on light. Nature 1997, 386, 143,  DOI: 10.1038/386143a0
    57. 57
      Zhao, J. M.; Zhang, Z. M. Electromagnetic energy storage and power dissipation in nanostructures. J. Quant. Spectrosc. Radiat. Transfer 2015, 151, 4957,  DOI: 10.1016/j.jqsrt.2014.09.011
    58. 58
      Zhang, Z. M. Nano/Microscale Heat Transfer; McGraw-Hill: New York, 2007.
    59. 59
      Palik, E. D. Handbook of Optical Constants of Solids; Palik, E. D., Ed.; Academic Press: San Diego, CA, 1998; Vol. 3 .
    60. 60
      Dieb, T. M.; Tsuda, K. Machine Learning-Based Experimental Design in Materials Science. In Nanoinformatics; Tanaka, I., Ed.; Springer: Singapore, 2018; pp 6574.
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