GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations.

@article{Fan2022GPUMDAP,
  title={GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations.},
  author={Zheyong Fan and Yanzhou Wang and Penghua Ying and Keke Song and Junjie Wang and Yong Wang and Zezhu Zeng and Ke Xu and Eric B. Lindgren and J. Magnus Rahm and Alexander J. Gabourie and Jiahui Liu and Haikuan Dong and Jianyang Wu and Yue Chen and Zheng Zhong and Jian Sun and Paul Erhart and Yan Jing Su and Tapio Ala‐Nissila},
  journal={The Journal of chemical physics},
  year={2022},
  volume={157 11},
  pages={
          114801
        }
}
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 (2021)] and their implementation in the open-source package gpumd. We increase the accuracy of NEP models both by improving the radial functions in the atomic-environment descriptor using a linear combination of Chebyshev basis functions and by extending the angular descriptor with some four-body and five-body… 

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