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physics.comp-ph2022★ 463 cited
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations
Zheyong Fan, Yanzhou Wang, Penghua Ying +17
We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2…
physics.comp-ph2021★ 526 cited
Neuroevolution machine learning potentials: Combining high accuracy and low cost in atomistic simulations and application to heat transport
Zheyong Fan, Zezhu Zeng, Cunzhi Zhang +4
We develop a neuroevolution-potential (NEP) framework for generating neural network based machine-learning potentials. They are trained using an evolutionary strategy for performin…