Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport

Zheyong Fan, Zezhu Zeng, Cunzhi Zhang, Yanzhou Wang, Keke Song, Haikuan Dong, Yue Chen, Tapio Ala-Nissila

Research output: Contribution to journalArticleScientificpeer-review

62 Citations (Scopus)
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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 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.

Original languageEnglish
Article number104309
Number of pages15
JournalPhysical Review B
Issue number10
Publication statusPublished - 18 Sept 2021
MoE publication typeA1 Journal article-refereed

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