General-purpose machine-learned potential for 16 elemental metals and their alloys
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arXiv
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866912135420837888 |
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| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_04732 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | General-purpose machine-learned potential for 16 elemental metals and their alloys 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 Materials Science Computational Physics 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. |
| title | General-purpose machine-learned potential for 16 elemental metals and their alloys |
| topic | Materials Science Computational Physics |
| url | https://arxiv.org/abs/2311.04732 |