Nature Communications (Nov 2024)
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,
- Zheyong Fan
Affiliations
- Keke Song
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing
- Rui Zhao
- School of Materials Science and Engineering, Hunan University
- Jiahui Liu
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing
- Yanzhou Wang
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing
- Eric Lindgren
- Chalmers University of Technology, Department of Physics
- Yong Wang
- National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University
- Shunda Chen
- Department of Civil and Environmental Engineering, George Washington University
- Ke Xu
- Department of Electronic Engineering and Materials Science and Technology Research Center, The Chinese University of Hong Kong
- Ting Liang
- Department of Electronic Engineering and Materials Science and Technology Research Center, The Chinese University of Hong Kong
- Penghua Ying
- Department of Physical Chemistry, School of Chemistry, Tel Aviv University
- Nan Xu
- Institute of Zhejiang University-Quzhou
- Zhiqiang Zhao
- State 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 Astronautics
- Jiuyang Shi
- National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University
- Junjie Wang
- National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University
- Shuang Lyu
- Department of Mechanical Engineering, The University of Hong Kong
- Zezhu Zeng
- Department of Mechanical Engineering, The University of Hong Kong
- Shirong Liang
- School of Science, Harbin Institute of Technology
- Haikuan Dong
- College of Physical Science and Technology, Bohai University
- Ligang Sun
- School of Science, Harbin Institute of Technology
- Yue Chen
- Department of Mechanical Engineering, The University of Hong Kong
- Zhuhua Zhang
- State 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 Astronautics
- Wanlin Guo
- State 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 Astronautics
- Ping Qian
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing
- Jian Sun
- National Laboratory of Solid State Microstructures, School of Physics and Collaborative Innovation Center of Advanced Microstructures, Nanjing University
- Paul Erhart
- Chalmers University of Technology, Department of Physics
- Tapio Ala-Nissila
- MSP group, QTF Centre of Excellence, Department of Applied Physics, Aalto University
- Yanjing Su
- Beijing Advanced Innovation Center for Materials Genome Engineering, University of Science and Technology Beijing
- Zheyong Fan
- College of Physical Science and Technology, Bohai University
- DOI
- https://doi.org/10.1038/s41467-024-54554-x
- Journal volume & issue
-
Vol. 15,
no. 1
pp. 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.
