General-purpose machine-learned potential for 16 elemental metals and their alloys

Keke Song, Rui Zhao, Jiahui Liu, Yanzhou Wang, Eric Lindgren, Yong Wang, Shunda Chen (), Ke Xu, Ting Liang, Penghua Ying, Nan Xu, Zhiqiang Zhao, Jiuyang Shi, Junjie Wang, Shuang Lyu, Zezhu Zeng, Shirong Liang, Haikuan Dong, Ligang Sun, Yue Chen, Zhuhua Zhang, Wanlin Guo, Ping Qian, Jian Sun (), Paul Erhart (), Tapio Ala-Nissila, Yanjing Su () and Zheyong Fan ()
Additional contact information
Keke Song: University of Science and Technology Beijing
Rui Zhao: Hunan University
Jiahui Liu: University of Science and Technology Beijing
Yanzhou Wang: University of Science and Technology Beijing
Eric Lindgren: Department of Physics
Yong Wang: Nanjing University
Shunda Chen: George Washington University
Ke Xu: The Chinese University of Hong Kong
Ting Liang: The Chinese University of Hong Kong
Penghua Ying: Tel Aviv University
Nan Xu: Institute of Zhejiang University-Quzhou
Zhiqiang Zhao: Nanjing University of Aeronautics and Astronautics
Jiuyang Shi: Nanjing University
Junjie Wang: Nanjing University
Shuang Lyu: The University of Hong Kong
Zezhu Zeng: The University of Hong Kong
Shirong Liang: Harbin Institute of Technology
Haikuan Dong: Bohai University
Ligang Sun: Harbin Institute of Technology
Yue Chen: The University of Hong Kong
Zhuhua Zhang: Nanjing University of Aeronautics and Astronautics
Wanlin Guo: Nanjing University of Aeronautics and Astronautics
Ping Qian: University of Science and Technology Beijing
Jian Sun: Nanjing University
Paul Erhart: Department of Physics
Tapio Ala-Nissila: Aalto University
Yanjing Su: University of Science and Technology Beijing
Zheyong Fan: Bohai University

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a promising approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach’s effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys.

Date: 2024
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DOI: 10.1038/s41467-024-54554-x

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