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

Misawa I. Masaaki; Shimamura H. Kohei; Shimojo H. Fuyuki

doi:10.4131/jshpreview.31.132

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

Misawa I. Masaaki, Shimamura H. Kohei, Shimojo H. Fuyuki

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	132-139
Number of pages	8
Journal	Review of High Pressure Science and Technology/Koatsuryoku No Kagaku To Gijutsu
Volume	31
Issue number	3
DOIs	https://doi.org/10.4131/jshpreview.31.132
Publication status	Published - 2021

Keywords

Deep learning
DFT
Molecular dynamics
Multiscale-shock technique

ASJC Scopus subject areas

Chemistry(all)
Materials Science(all)
Condensed Matter Physics

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10.4131/jshpreview.31.132

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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.",

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author = "Masaaki, {Misawa I.} and Kohei, {Shimamura H.} and Fuyuki, {Shimojo H.}",

year = "2021",

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language = "English",

volume = "31",

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AU - Kohei, Shimamura H.

AU - Fuyuki, Shimojo H.

PY - 2021

Y1 - 2021

N2 - 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.

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KW - DFT

KW - Molecular dynamics

KW - Multiscale-shock technique

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

Abstract

Keywords

ASJC Scopus subject areas

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