Molecular Dynamics Simulation of Shock Compression Behavior Based on First-Principles Calculation and Machine-Learning
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- MISAWA Masaaki
- Graduate School of Natural Science and Technology, Okayama University
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- SHIMAMURA Kohei
- Department of Physics, Kumamoto University
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- SHIMOJO Fuyuki
- Department of Physics, Kumamoto University
Bibliographic Information
- Other Title
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- 第一原理計算と機械学習に基づく衝撃圧縮挙動の分子動力学計算
- ダイイチ ゲンリ ケイサン ト キカイ ガクシュウ ニ モトズク ショウゲキ アッシュク キョドウ ノ ブンシ ドウリキガク ケイサン
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Description
<p>Artificial neural network (ANN) potential, which is an interatomic potential constructed by machine-leaning, attracts attention as a promising method to achieve extra-large-scale molecular dynamics (MD) simulation with first-principles accuracy. Application of this ANN-MD to far-from-equilibrium phenomena is very important in not only materials science but also high-pressure research field. In this article, a research example of ANN-MD simulation for elastic- and plastic-shock compression behavior in crystalline silica was described.</p>
Journal
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- The Review of High Pressure Science and Technology
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The Review of High Pressure Science and Technology 31 (3), 132-139, 2021
The Japan Society of High Pressure Science and Technology
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Details 詳細情報について
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- CRID
- 1390291115030040576
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- NII Article ID
- 130008159019
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- NII Book ID
- AN10452913
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- ISSN
- 13481940
- 0917639X
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- NDL BIB ID
- 032003299
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
- KAKEN
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- Abstract License Flag
- Disallowed
