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
复制标题
深势分子动力学：具有量子力学准确性的可扩展模型
DOI：
10.1103/physrevlett.120.143001
复制
发表时间：
2018-04-04
影响因子：
8.6
通讯作者：
Weinan, E.
中科院分区：
文献类型：
--
作者：
Zhang, Linfeng;Han, Jiequn;Weinan, E.
我们介绍了一种分子模拟方案，即深势分子动力学（DPMD）方法，该方法基于由从头算数据训练的精心制作的深度神经网络产生的多体势和原子间力。神经网络模型保留了问题中的所有自然对称性。它是基于第一原理的，因为除了网络模型之外，还有其他组件。我们表明，该方案提供了一个有效的和准确的协议，在各种系统，包括散装材料和分子。在所有这些情况下，DPMD给出的结果基本上与原始数据无法区分，其成本与系统大小呈线性关系。
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 withab initiodata. The neural network model preserves all the natural symmetries in the problem. It is first-principles based in the sense that there are noad hoccomponents 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.