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

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

Phys. Rev. Lett. 120, 143001 – Published 4 April 2018

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.

Received 3 August 2017

DOI:https://doi.org/10.1103/PhysRevLett.120.143001

Physics Subject Headings (PhySH)

Biological neural networks Classical statistical mechanics Density functional theory First-principles calculations Interatomic & molecular potentials Learning Potential energy surfaces Solid-state chemistry Thermal properties Van der Waals interaction

Molecular dynamics

Statistical Physics & ThermodynamicsPhysics of Living SystemsInterdisciplinary PhysicsCondensed Matter, Materials & Applied PhysicsAtomic, Molecular & Optical

Authors & Affiliations

Linfeng Zhang and Jiequn Han

Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA

Han Wang^*

Institute of Applied Physics and Computational Mathematics, Fenghao East Road 2, Beijing 100094, People’s Republic of China and CAEP Software Center for High Performance Numerical Simulation, Huayuan Road 6, Beijing 100088, People’s Republic of China

Roberto Car

Department of Chemistry, Department of Physics, Program in Applied and Computational Mathematics, Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, New Jersey 08544, USA

Weinan E^†

Department of Mathematics and Program in Applied and Computational Mathematics, Princeton University, Princeton, New Jersey 08544, USA and Center for Data Science, Beijing International Center for Mathematical Research, Peking University, Beijing Institute of Big Data Research, Beijing 100871, People’s Republic of China