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

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

8 Nov 2023arXiv:2311.04732links table onlyarchive 2025-07-28

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, Zheyong Fan

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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 feasible 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. This work represents a significant leap towards a unified general-purpose MLP encompassing the periodic table, with profound implications for materials science.

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