Journal
PHYSICAL REVIEW LETTERS
Volume 120, Issue 14, Pages -Publisher
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.120.143001
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Funding
- NNSFC [91130005]
- ONR [N00014-13-1-0338]
- DOE [DE-SC0008626, DE-SC0009248]
- NSFC [U1430237]
- DOE-SciDAC Grant [DE-SC0008626]
- National Science Foundation of China [11501039, 91530322]
- National Key Research and Development Program of China [2016YFB0201200, 2016YFB0201203]
- Science Challenge Project [JCKY2016212A502]
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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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