| アイテムタイプ |
共通アイテムタイプ(1) |
| 公開日 |
2025-09-19 |
| タイトル |
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タイトル |
Neural-network interatomic potential for grain boundary structures and their energetics in silicon |
|
言語 |
en |
| 著者 |
Yokoi, T.
野田, 祐輔
Nakamura, A.
Matsunaga, K.
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| 著作権関連情報 |
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|
権利情報 |
Copyright (c) 2020 American Physical Society |
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言語 |
en |
| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
Artificial neural-network (ANN) interatomic potentials for simulating atomic structures and energetics of grain boundaries (GBs) in silicon were constructed and integrated into structural optimization and molecular dynamics (MD) algorithms. A training dataset including various atomic environments of symmetric tilt GBs was generated by performing density-functional-theory (DFT) calculations. The ANN potential after training was found to be capable of approximating the potential-energy surface at GBs even with dangling bonds and large atomic displacements at high temperatures, which cannot be well reproduced with empirical interatomic potentials. Additionally, reliability of the ANN potential for molecular simulations was evaluated. GB structures optimized or equilibrated by the ANN molecular simulations were also energetically lower for DFT calculations, without significant errors. The ANN potential is therefore expected to greatly reduce structural-optimization iterations and required time steps to acquire stable or equilibrium GB structures in MD simulations, enabling us to address even large-scale systems of general GBs in silicon, with high accuracy and low computational cost. |
|
言語 |
en |
| 書誌情報 |
en : Physical Review Materials
巻 4,
号 1,
p. 014605,
発行日 2020-01-29
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| 出版社 |
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出版者 |
American Physical Society |
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言語 |
en |
| 言語 |
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|
言語 |
eng |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| 出版タイプ |
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|
出版タイプ |
VoR |
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出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| DOI |
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|
識別子タイプ |
DOI |
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|
関連識別子 |
https://doi.org/10.1103/PhysRevMaterials.4.014605 |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2475-9953 |
| 研究者情報 |
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|
URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/100001765_ja.html |
| 論文ID（連携） |
|
|
値 |
10463663 |
| 連携ID |
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|
値 |
15110 |
10463663.pdf (4.7 MB)
