| アイテムタイプ |
itemtype_ver1(1) |
| 公開日 |
2026-03-27 |
| タイトル |
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タイトル |
Nonequilibrium molecular dynamics of ion conduction with equivariant neural network models |
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言語 |
en |
| 著者 |
Minami, Saori
Kutana, Alex
Jinnouchi, Ryosuke
Asahi, Ryoji
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| アクセス権 |
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アクセス権 |
open access |
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アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 権利 |
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権利情報 |
© 2025 American Physical Society |
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言語 |
en |
| 内容記述 |
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内容記述タイプ |
Abstract |
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内容記述 |
We propose a method for evaluating ionic conductivity by integrating machine learning models with nonequilibrium molecular dynamics simulations under a constant electric field. The method computes the forces exerted on atoms by the external electric field within the linear response regime, using the Born effective charge tensor predicted by an equivariant graph convolutional neural network. These field-induced forces are then combined with nonperturbed forces predicted by an equivariant neural network potential. We applied this approach to Li10GeP2S12, a representative solid lithium-ion electrolyte. The method enables accurate evaluation of conductivity at a level comparable to first-principles molecular dynamics simulations, but at a fraction of their prohibitive computational cost, and further allows simulations of realistic ion conduction dynamics under an applied electric field. The predicted Born effective charges indicate that Li ions located near GeS4 and PS4 tetrahedra exhibit higher ionization. These ions are localized around GeS4 and PS4 tetrahedra, which serve as structural barriers that interrupt conduction pathways. In contrast, other Li ions are less ionized and primarily contribute to ionic conduction. The proposed method offers a nonarbitrary and physically interpretable framework for the quantitative evaluation of ionic dynamics, including charge fluctuations that are essential for complex systems such as inhomogeneous crystals and interfaces. |
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言語 |
en |
| 出版者 |
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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resource |
http://purl.org/coar/resource_type/c_6501 |
|
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 |
| 関連情報 |
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関連タイプ |
isVersionOf |
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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/fll6-v5fx |
| 収録物識別子 |
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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 |
| 書誌情報 |
en : Physical Review Materials
巻 9,
p. 103802,
発行日 2025-10-15
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fll6-v5fx.pdf (1.8 MB)
