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

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.

Keywords

Affiliated Institutions

• Beijing Institute of Big Data Research CN
• Institute of Applied Physics and Computational Mathematics CN
• Numerical Method (China) HK
• Peking University CN
• Princeton University US

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