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

Masaaki Misawa, Shogo Fukushima, Akihide Koura, Kohei Shimamura, Fuyuki Shimojo, Subodh Tiwari, Ken Ichi Nomura, Rajiv K. Kalia, Aiichiro Nakano, Priya Vashishta

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The use of artificial neural network (ANN) potentials trained with first-principles calculations has emerged as a promising approach for molecular dynamics (MD) simulations encompassing large space and time scales while retaining first-principles accuracy. To date, however, the application of ANN-MD has been limited to near-equilibrium processes. Here we combine first-principles-trained ANN-MD with multiscale shock theory (MSST) to successfully describe far-from-equilibrium shock phenomena. Our ANN-MSST-MD approach describes shock-wave propagation in solids with first-principles accuracy but a 5000 times shorter computing time. Accordingly, ANN-MD-MSST was able to resolve fine, long-time elastic deformation at low shock speed, which was impossible with first-principles MD because of the high computational cost. This work thus lays a foundation of ANN-MD simulation to study a wide range of far-from-equilibrium processes.

Original languageEnglish
Pages (from-to)4536-4541
Number of pages6
JournalJournal of Physical Chemistry Letters
Volume11
Issue number11
DOIs
Publication statusPublished - Jun 4 2020

ASJC Scopus subject areas

  • Materials Science(all)
  • Physical and Theoretical Chemistry

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