TY - JOUR
T1 - Deep Potential Molecular Dynamics
T2 - A Scalable Model with the Accuracy of Quantum Mechanics
AU - Zhang, Linfeng
AU - Han, Jiequn
AU - Wang, Han
AU - Car, Roberto
AU - E, Weinan
N1 - Publisher Copyright:
© 2018 American Physical Society.
PY - 2018/4/4
Y1 - 2018/4/4
N2 - We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.
AB - We introduce a scheme for molecular simulations, the deep potential molecular dynamics (DPMD) method, based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with ab initio data. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are no ad hoc components aside from the network model. We show that the proposed scheme provides an efficient and accurate protocol in a variety of systems, including bulk materials and molecules. In all these cases, DPMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.
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U2 - 10.1103/PhysRevLett.120.143001
DO - 10.1103/PhysRevLett.120.143001
M3 - Article
C2 - 29694129
AN - SCOPUS:85045006696
SN - 0031-9007
VL - 120
JO - Physical review letters
JF - Physical review letters
IS - 14
M1 - 143001
ER -