General-purpose machine-learned potential for 16 elemental metals and their alloys
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-024-54554-x |
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| author | 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 |
| author_facet | 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 |
| author_sort | Keke Song |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1e32fd69fc87413d975d70ce7e63b487 |
| institution | OA Journals |
| issn | 2041-1723 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-1e32fd69fc87413d975d70ce7e63b4872025-08-20T02:08:20ZengNature PortfolioNature Communications2041-17232024-11-0115111510.1038/s41467-024-54554-xGeneral-purpose machine-learned potential for 16 elemental metals and their alloysKeke Song0Rui Zhao1Jiahui Liu2Yanzhou Wang3Eric Lindgren4Yong Wang5Shunda Chen6Ke Xu7Ting Liang8Penghua Ying9Nan Xu10Zhiqiang Zhao11Jiuyang Shi12Junjie Wang13Shuang Lyu14Zezhu Zeng15Shirong Liang16Haikuan Dong17Ligang Sun18Yue Chen19Zhuhua Zhang20Wanlin Guo21Ping Qian22Jian Sun23Paul Erhart24Tapio Ala-Nissila25Yanjing Su26Zheyong Fan27Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology BeijingSchool of Materials Science and Engineering, Hunan UniversityBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology BeijingBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology BeijingChalmers University of Technology, Department of PhysicsNational Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing UniversityDepartment of Civil and Environmental Engineering, George Washington UniversityDepartment of Electronic Engineering and Materials Science and Technology Research Center, The Chinese University of Hong KongDepartment of Electronic Engineering and Materials Science and Technology Research Center, The Chinese University of Hong KongDepartment of Physical Chemistry, School of Chemistry, Tel Aviv UniversityInstitute of Zhejiang University-QuzhouState Key Laboratory of Mechanics and Control of Mechanical Structures, Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, and Institute for Frontier Science, Nanjing University of Aeronautics and AstronauticsNational Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing UniversityNational Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing UniversityDepartment of Mechanical Engineering, The University of Hong KongDepartment of Mechanical Engineering, The University of Hong KongSchool of Science, Harbin Institute of TechnologyCollege of Physical Science and Technology, Bohai UniversitySchool of Science, Harbin Institute of TechnologyDepartment of Mechanical Engineering, The University of Hong KongState Key Laboratory of Mechanics and Control of Mechanical Structures, Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, and Institute for Frontier Science, Nanjing University of Aeronautics and AstronauticsState Key Laboratory of Mechanics and Control of Mechanical Structures, Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education, and Institute for Frontier Science, Nanjing University of Aeronautics and AstronauticsBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology BeijingNational Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing UniversityChalmers University of Technology, Department of PhysicsMSP group, QTF Centre of Excellence, Department of Applied Physics, Aalto UniversityBeijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology BeijingCollege of Physical Science and Technology, Bohai UniversityAbstract 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.https://doi.org/10.1038/s41467-024-54554-x |
| spellingShingle | 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 General-purpose machine-learned potential for 16 elemental metals and their alloys Nature Communications |
| title | General-purpose machine-learned potential for 16 elemental metals and their alloys |
| title_full | General-purpose machine-learned potential for 16 elemental metals and their alloys |
| title_fullStr | General-purpose machine-learned potential for 16 elemental metals and their alloys |
| title_full_unstemmed | General-purpose machine-learned potential for 16 elemental metals and their alloys |
| title_short | General-purpose machine-learned potential for 16 elemental metals and their alloys |
| title_sort | general purpose machine learned potential for 16 elemental metals and their alloys |
| url | https://doi.org/10.1038/s41467-024-54554-x |
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