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20192026
most citedGPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

463 citations · 701 across the 5 of their papers we have counts for

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physics.comp-ph2024★ 35 cited

E(n)-Equivariant Cartesian Tensor Passing Potential

Junjie Wang, Yong Wang, Haoting Zhang +6

Machine learning potential (MLP) has been a popular topic in recent years for its potential to replace expensive first-principles calculations in some large systems. Meanwhile, mes…

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…