Application of First-Principles-Based Artificial Neural Network Potentials to Multiscale-Shock Dynamics Simulations on Solid Materials-Reference-Cited by-同舟云学术

Application of First-Principles-Based Artificial Neural Network Potentials to Multiscale-Shock Dynamics Simulations on Solid Materials

Published:2020-05-22 Issue:11 Volume:11 Page:4536-4541
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Misawa Masaaki¹^ORCID,Fukushima Shogo²,Koura Akihide²,Shimamura Kohei²^ORCID,Shimojo Fuyuki²,Tiwari Subodh³^ORCID,Nomura Ken-ichi³,Kalia Rajiv K.³,Nakano Aiichiro³^ORCID,Vashishta Priya³^ORCID

Affiliation:

1. Graduate School of Natural Science and Technology, Okayama University, Okayama 700-8530, Japan

2. Department of Physics, Kumamoto University, Kumamoto 860-8555, Japan

3. Collaboratory for Advanced Computing and Simulations, University of Southern California, Los Angeles, California 90089, United States

Funder

Basic Energy Sciences

Core Research for Evolutional Science and Technology

Japan Society for the Promotion of Science

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.0c00637

Reference39 articles.

1. Nanosecond homogeneous nucleation and crystal growth in shock-compressed SiO2

2. Curious kinetic behavior in silica polymorphs solves seifertite puzzle in shocked meteorite

3. Equation of state of stishovite and interpretation of SiO2shock-compression data

4. Ultrafast transformation of graphite to diamond: An ab initio study of graphite under shock compression

5. Ultrafast visualization of crystallization and grain growth in shock-compressed SiO2

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