Thermal conductivity calculation based on Green–Kubo formula using ANN potential for β-Ag2Se

Takeshita, Yusuke; Shimamura, Kohei; Fukushima, Satoshi; Koura, Akihide; Shimojo, Fuyuki

doi:10.1016/j.jpcs.2022.110580

Cited by 18 publications

(5 citation statements)

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“…For this purpose, larger spatial/ time scale than FPMD and higher accuracy than classical MD is required. With a recent progress of machineleaning interatomic potentials (MLIPs), such large-scale molecular simulations of metal chalcogenides with high accuracy are becoming feasible [24][25][26][27][28] . We plan to apply advanced computational techniques such as MLIPs for further investigations of Ag 2 S systems.…”

Section: Resultsmentioning

confidence: 99%

Defect-free and crystallinity-preserving ductile deformation in semiconducting Ag2S

Misawa

Hokyo

Fukushima

et al. 2022

Sci Rep

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Typical ductile materials are metals, which deform by the motion of defects like dislocations in association with non-directional metallic bonds. Unfortunately, this textbook mechanism does not operate in most inorganic semiconductors at ambient temperature, thus severely limiting the development of much-needed flexible electronic devices. We found a shear-deformation mechanism in a recently discovered ductile semiconductor, monoclinic-silver sulfide (Ag2S), which is defect-free, omni-directional, and preserving perfect crystallinity. Our first-principles molecular dynamics simulations elucidate the ductile deformation mechanism in monoclinic-Ag2S under six types of shear systems. Planer mass movement of sulfur atoms plays an important role for the remarkable structural recovery of sulfur-sublattice. This in turn arises from a distinctively high symmetry of the anion-sublattice in Ag2S, which is not seen in other brittle silver chalcogenides. Such mechanistic and lattice-symmetric understanding provides a guideline for designing even higher-performance ductile inorganic semiconductors.

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Section: Resultsmentioning

confidence: 99%

Defect-free and crystallinity-preserving ductile deformation in semiconducting Ag2S

Misawa

Hokyo

Fukushima

et al. 2022

Sci Rep

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“…( II.6) was that its contribution to the TC was dominant compared to that of the kinetic part in our previous studies regarding silver chalcogenides. [30,31] The effect of the regularization term on training is controlled by the magnitude of its coefficient p J . In this study, in addition to p j = 0.0, we mainly set p J = 10 -4 , 10 -3 , 10 -2 , or 10 -1 to construct ANN potentials and compare their results.…”

Section: Regularization Term Of Heat Fluxmentioning

confidence: 99%

“…We recently proposed a computational framework for calculating TC by the GK method using ANN potentials, reporting the possibility of estimating the TCs of β -and α-Ag 2 Se with high accuracy. [30][31][32] In the present study, we further improved the framework by the heat flux regularization.…”

Section: Introductionmentioning

confidence: 98%

Construction of Machine-Learning Interatomic Potential Under Heat Flux Regularization and Its Application to Power Spectrum Analysis for Silver Chalcogenides

Shimamura¹,

Koura²,

Shimojo³

2022

Preprint

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We propose a data-driven approach for constructing machine-learning interatomic potentials (MLIPs) trained under a regularization with the aim of avoiding nonphysical heat flux. Specifically, we introduce a regularization term for the heat flux into the cost function of MLIPs to be minimized. Since the treatment of heat flux using MLIPs with regularization can be decomposed into elemental contributions or conducted in frequency space, this approach is expected to be useful for investigating the origin of thermal conductivity obtained from the Green-Kubo formula. However, the strength of regularization needs to be appropriately set because it may reduce not only the nonphysical part but also the intrinsic heat flux one. To this end, we investigated the conditions for constructing MLIPs that can reproduce the power spectra of heat flux associated with the empirical interatomic potential of Ag 2 Se, which consists of pairwise functions and do not contain a nonphysical heat flux. The appropriate strength could be estimated from the variation of the magnitude of regularization term as well as root mean square errors for total potential energy, atomic force, and virial stress with respect to the strengths, without reference spectrum data. As an application example, we explored the differences in power spectra between superionic and nonsuperionic conducting phases based on the heat flux regularization to MLIPs trained with the first-principles calculation data of Ag 2 S. Furthermore, our results demonstrate that training with the regularization improves the robustness of MLIPs as well as the reduction of the nonphysical heat flux.

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“…[14,15] First-principle calculations have reduced the computation time by introducing artificial NN potentials. [16][17][18][19][20] Learning pseudo-microscopic images has dramatically shortened and improved the determination of the physical properties of crystal growth. [21] Furthermore, ML has been applied to EBSD methods.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Elucidation of Physical Properties of Polycrystalline Materials Using Multichannel Machine Learning of Electron Backscattering Diffraction

Nozawa,

Ishiyama,

Suemasu

et al. 2024

Adv Elect Materials

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The application of machine learning in materials science has yielded several benefits, including the prediction of physical properties and the improvement of experimental efficiency. However, with complex models, such as convolutional neural networks (CNN), learning has become a black box, from which no universal physical knowledge can be obtained. In this study, a highly accurate prediction of the electrical properties of polycrystalline semiconductor thin films is achieved by learning multichannel CNN models from electron backscattering diffraction (EBSD) data, that is band contrasts, grain boundaries, and inverse pole figures. In addition, it examines how the CNN model learned the correlation between the crystallinity, grain boundaries, crystallographic orientation, and carrier mobility by polarizing certain EBSD data and checking the predicted changes in carrier mobility. Physical parameters affecting carrier mobility can be extracted, which is challenging via human image recognition. The methods proposed in this study will not only enable the prediction of electrical properties from EBSD data for all materials but also will contribute to the discovery of complex physical phenomena beyond the limits of human analysis.

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Thermal conductivity calculation based on Green–Kubo formula using ANN potential for β-Ag2Se

Cited by 18 publications

References 50 publications

Defect-free and crystallinity-preserving ductile deformation in semiconducting Ag2S

Defect-free and crystallinity-preserving ductile deformation in semiconducting Ag2S

Construction of Machine-Learning Interatomic Potential Under Heat Flux Regularization and Its Application to Power Spectrum Analysis for Silver Chalcogenides

Prediction and Elucidation of Physical Properties of Polycrystalline Materials Using Multichannel Machine Learning of Electron Backscattering Diffraction

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