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Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

2018; American Physical Society; Volume: 120; Issue: 14 Linguagem: Inglês

10.1103/physrevlett.120.143001

1092-0145

Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, E Weinan,

Quantum, superfluid, helium dynamics

We introduce a scheme for molecular simulations, the Deep Potential Molecular Dynamics (DeePMD) 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 principle-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, DeePMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.