期刊
PHYSICAL REVIEW B
卷 104, 期 10, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevB.104.104309
关键词
-
资金
- National Natural Science Foundation of China (NSFC) [11974059]
- Academy of Finland Centre of Excellence program QTF [312298]
In this research, a neuroevolution-potential framework for generating neural network-based machine-learning potentials trained with an evolutionary strategy is developed. The atomic environment descriptor is constructed using Chebyshev and Legendre polynomials. The NEP method, implemented in GPUMD package, achieves high computational speed and provides per-atom heat current information.
We develop a neuroevolution-potential (NEP) framework for generating neural network-based machine-learning potentials. They are trained using an evolutionary strategy for performing large-scale molecular dynamics (MD) simulations. A descriptor of the atomic environment is constructed based on Chebyshev and Legendre polynomials. The method is implemented in graphic processing units within the open-source GPUMD package, which can attain a computational speed over 10(7) atom-step per second using one Nvidia Tesla V100. Furthermore, per-atom heat current is available in NEP, which paves the way for efficient and accurate MD simulations of heat transport in materials with strong phonon anharmonicity or spatial disorder, which usually cannot be accurately treated either with traditional empirical potentials or with perturbative methods.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据