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

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posted on 2022-10-18, 11:23 authored by Zheyong Fan, Yanzhou Wang, Penghua Ying, Keke Song, Junjie Wang, Yong Wang, Zezhu Zeng, Ke Xu, Eric Lindgren, J Magnus Rahm, Alexander J Gabourie, Jiahui Liu, Haikuan Dong, Jianyang Wu, Yue Chen, Zheng Zhong, Jian Sun, Paul Erhart, Yanjing Su, Tapio Ala-NissilaTapio Ala-Nissila
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 contributions as in the atomic cluster expansion approach. We also detail our efficient implementation of the NEP approach in graphics processing units as well as our workflow for the construction of NEP models and demonstrate their application in large-scale atomistic simulations. By comparing to state-of-the-art MLPs, we show that the NEP approach not only achieves above-average accuracy but also is far more computationally efficient. These results demonstrate that the gpumd package is a promising tool for solving challenging problems requiring highly accurate, large-scale atomistic simulations. To enable the construction of MLPs using a minimal training set, we propose an active-learning scheme based on the latent space of a pre-trained NEP model. Finally, we introduce three separate Python packages, viz., gpyumd, calorine, and pynep, that enable the integration of gpumd into Python workflows.

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School

  • Science

Department

  • Mathematical Sciences

Published in

The Journal of Chemical Physics

Volume

157

Issue

11

Publisher

AIP Publishing

Version

  • AM (Accepted Manuscript)

Rights holder

© Authors

Publisher statement

This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Zheyong Fan, Yanzhou Wang, Penghua Ying, Keke Song, Junjie Wang, Yong Wang, Zezhu Zeng, Ke Xu, Eric Lindgren, J. Magnus Rahm, Alexander J. Gabourie, Jiahui Liu, Haikuan Dong, Jianyang Wu, Yue Chen, Zheng Zhong, Jian Sun, Paul Erhart, Yanjing Su, and Tapio Ala-Nissila , "GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations", J. Chem. Phys. 157, 114801 (2022) https://doi.org/10.1063/5.0106617 and may be found at https://doi.org/10.1063/5.0106617

Acceptance date

2022-08-24

Publication date

2022-09-20

Copyright date

2022

ISSN

0021-9606

eISSN

1089-7690

Language

  • en

Depositor

Prof Tapio Ala-Nissila. Deposit date: 17 October 2022

Article number

114801

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