GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations Article Swipe
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-Nissilä
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1063/5.0106617
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1063/5.0106617
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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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1063/5.0106617
- OA Status
- green
- Cited By
- 297
- References
- 161
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4281385937
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4281385937Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1063/5.0106617Digital Object Identifier
- Title
-
GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulationsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-24Full publication date if available
- Authors
-
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-NissiläList of authors in order
- Landing page
-
https://doi.org/10.1063/5.0106617Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2205.10046Direct OA link when available
- Concepts
-
Python (programming language), Computer science, Workflow, Computational science, Graphics, Artificial intelligence, Machine learning, Algorithm, Programming language, Computer graphics (images), DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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297Total citation count in OpenAlex
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2025: 159, 2024: 76, 2023: 53, 2022: 9Per-year citation counts (last 5 years)
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161Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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