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

“Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics” (2018), by Linfeng Zhang et al. Materials Science research paper. Original abstract below, unchanged. A free PDF is available.

- Authors: Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, E Weinan
- Published: April 4, 2018
- Field: Materials Science
- DOI: https://doi.org/10.1103/physrevlett.120.143001
- Free PDF: https://arxiv.org/pdf/1707.09571

## 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.

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