计算机科学
非谐性
人工神经网络
勒让德多项式
神经进化
分子动力学
人工智能
Atom(片上系统)
切比雪夫滤波器
算法
计算科学
物理
化学
计算化学
并行计算
量子力学
计算机视觉
作者
Zheyong Fan,Zezhu Zeng,Cunzhi Zhang,Yanzhou Wang,Keke Song,Haikuan Dong,Yue Chen,Tapio Ala-Nissilä
出处
期刊:Physical review
日期:2021-09-20
卷期号:104 (10)
被引量:153
标识
DOI:10.1103/physrevb.104.104309
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI