Molecular Dynamics Simulation of Shock Compression Behavior Based on First-Principles Calculation and Machine-Learning

• MISAWA Masaaki

  Graduate School of Natural Science and Technology, Okayama University

• SHIMAMURA Kohei

  Department of Physics, Kumamoto University

• SHIMOJO Fuyuki

  Department of Physics, Kumamoto University

Bibliographic Information

Other Title

• 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

Abstract

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.

Journal

• The Review of High Pressure Science and Technology

  The Review of High Pressure Science and Technology 31 (3), 132-139, 2021

  The Japan Society of High Pressure Science and Technology

Keywords

• DFT
• deep learning
• molecular dynamics
• multiscale-shock technique

Details

• CRID: 1390291115030040576
• NII Article ID: 130008159019
• NII Book ID: AN10452913
• DOI: 10.4131/jshpreview.31.132
• ISSN: 13481940
• ISSN: 0917639X
• NDL BIB ID: 032003299
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I032003299
• Web Site: https://www.jstage.jst.go.jp/article/jshpreview/31/3/31_132/_pdf
• Text Lang: ja
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed