General-purpose machine-learned potential for 16 elemental metals and their alloys
| Autor: | Song, Keke, Zhao, Rui, Liu, Jiahui, Wang, Yanzhou, Lindgren, Eric, Wang, Yong, Chen, Shunda, Xu, Ke, Liang, Ting, Ying, Penghua, Xu, Nan, Zhao, Zhiqiang, Shi, Jiuyang, Wang, Junjie, Lyu, Shuang, Zeng, Zezhu, Liang, Shirong, Dong, Haikuan, Sun, Ligang, Chen, Yue, Zhang, Zhuhua, Guo, Wanlin, Qian, Ping, Sun, Jian, Erhart, Paul, Ala-Nissila, Tapio, Su, Yanjing, Fan, Zheyong |
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| Rok vydání: | 2023 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Main text with 17 pages and 8 figures; supplementary with 26 figures and 4 tables; source code and training/test data available |
| Databáze: | arXiv |
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