TY - JOUR
T1 - Application of First-Principles-Based Artificial Neural Network Potentials to Multiscale-Shock Dynamics Simulations on Solid Materials
AU - Misawa, Masaaki
AU - Fukushima, Shogo
AU - Koura, Akihide
AU - Shimamura, Kohei
AU - Shimojo, Fuyuki
AU - Tiwari, Subodh
AU - Nomura, Ken Ichi
AU - Kalia, Rajiv K.
AU - Nakano, Aiichiro
AU - Vashishta, Priya
PY - 2020/6/4
Y1 - 2020/6/4
N2 - 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.
AB - 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.
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U2 - 10.1021/acs.jpclett.0c00637
DO - 10.1021/acs.jpclett.0c00637
M3 - Article
C2 - 32443935
AN - SCOPUS:85085962484
VL - 11
SP - 4536
EP - 4541
JO - Journal of Physical Chemistry Letters
JF - Journal of Physical Chemistry Letters
SN - 1948-7185
IS - 11
ER -