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

526 citations · 989 across the 2 of their papers we have counts for

collaborators

2 papers

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…