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

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

Research output: Contribution to journalArticlepeer-review

188 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number143001
JournalPhysical review letters
Volume120
Issue number14
DOIs
StatePublished - Apr 4 2018

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

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