Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, Weinan E
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- github.com/mindspore-ai/models/tree/master/research/hpc/molecular_dynamicsmindspore★ 0
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- github.com/yangyucheng000/molecular_dynamicsmindspore★ 0
- github.com/2023-MindSpore-1/ms-code-218/tree/main/molecular_dynamicsmindspore★ 0
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Abstract
We introduce a scheme for molecular simulations, the Deep Potential Molecular Dynamics (DeePMD) 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 principle-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, DeePMD gives results that are essentially indistinguishable from the original data, at a cost that scales linearly with system size.