start-ver=1.4
cd-journal=joma
no-vol=11
cd-vols=
no-issue=11
article-no=
start-page=4536
end-page=4541
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2020
dt-pub=20200522
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Application of First-Principles-Based Artificial Neural Network Potentials to Multiscale-Shock Dynamics Simulations on Solid Materials
en-subtitle=
kn-subtitle=
en-abstract=
kn-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.
en-copyright=
kn-copyright=

en-aut-name=MisawaMasaaki
en-aut-sei=Misawa
en-aut-mei=Masaaki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=FukushimaShogo
en-aut-sei=Fukushima
en-aut-mei=Shogo
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=KouraAkihide
en-aut-sei=Koura
en-aut-mei=Akihide
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

en-aut-name=ShimamuraKohei
en-aut-sei=Shimamura
en-aut-mei=Kohei
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=4
ORCID=

en-aut-name=ShimojoFuyuki
en-aut-sei=Shimojo
en-aut-mei=Fuyuki
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=5
ORCID=

en-aut-name=TiwariSubodh
en-aut-sei=Tiwari
en-aut-mei=Subodh
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=6
ORCID=

en-aut-name=NomuraKen-ichi
en-aut-sei=Nomura
en-aut-mei=Ken-ichi
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=7
ORCID=

en-aut-name=KaliaRajiv K.
en-aut-sei=Kalia
en-aut-mei=Rajiv K.
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=8
ORCID=

en-aut-name=NakanoAiichiro
en-aut-sei=Nakano
en-aut-mei=Aiichiro
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=9
ORCID=

en-aut-name=VashishtaPriya
en-aut-sei=Vashishta
en-aut-mei=Priya
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=10
ORCID=

affil-num=1
en-affil=Graduate School of Natural Science and Technology, Okayama University
kn-affil=

affil-num=2
en-affil=Department of Physics, Kumamoto University
kn-affil=

affil-num=3
en-affil=Department of Physics, Kumamoto University
kn-affil=

affil-num=4
en-affil=Department of Physics, Kumamoto University
kn-affil=

affil-num=5
en-affil=Department of Physics, Kumamoto University
kn-affil=

affil-num=6
en-affil=Collaboratory for Advanced Computing and Simulations, University of Southern California
kn-affil=

affil-num=7
en-affil=Collaboratory for Advanced Computing and Simulations, University of Southern California
kn-affil=

affil-num=8
en-affil=Collaboratory for Advanced Computing and Simulations, University of Southern California
kn-affil=

affil-num=9
en-affil=Collaboratory for Advanced Computing and Simulations, University of Southern California
kn-affil=

affil-num=10
en-affil=Collaboratory for Advanced Computing and Simulations, University of Southern California
kn-affil=
END