Machine learning potential aided structure search for low-lying candidates of Au clusters

doi:10.1088/1674-1056/ac5c3d

中国物理B ›› 2022, Vol. 31 ›› Issue (7): 78402-078402.doi: 10.1088/1674-1056/ac5c3d

Machine learning potential aided structure search for low-lying candidates of Au clusters

Tonghe Ying(应通和)^1,2, Jianbao Zhu(朱健保)^1,2,†, and Wenguang Zhu(朱文光)^1,2

1 Department of Physics, University of Science and Technology of China, and Key Laboratory of Strongly-Coupled Quantum Matter Physics, Chinese Academy of Sciences, Hefei 230026, China;
2 International Center for Quantum Design of Functional Materials(ICQD), Hefei National Laboratory for Physical Sciences at the Microscale, and Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China

收稿日期:2021-12-23 修回日期:2022-03-04 接受日期:2022-03-10 出版日期:2022-06-09 发布日期:2022-07-19
通讯作者: Jianbao Zhu E-mail:jianbzhu@ustc.edu.cn
基金资助:
Project supported by the National Key Research and Development Program of China (Grant Nos. 2017YFA0204904 and 2019YFA0210004).

Machine learning potential aided structure search for low-lying candidates of Au clusters

Tonghe Ying(应通和)^1,2, Jianbao Zhu(朱健保)^1,2,†, and Wenguang Zhu(朱文光)^1,2

1 Department of Physics, University of Science and Technology of China, and Key Laboratory of Strongly-Coupled Quantum Matter Physics, Chinese Academy of Sciences, Hefei 230026, China;
2 International Center for Quantum Design of Functional Materials(ICQD), Hefei National Laboratory for Physical Sciences at the Microscale, and Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei 230026, China

Received:2021-12-23 Revised:2022-03-04 Accepted:2022-03-10 Online:2022-06-09 Published:2022-07-19
Contact: Jianbao Zhu E-mail:jianbzhu@ustc.edu.cn
Supported by:
Project supported by the National Key Research and Development Program of China (Grant Nos. 2017YFA0204904 and 2019YFA0210004).

摘要/Abstract

摘要： A machine learning (ML) potential for Au clusters is developed through training on a dataset including several different sized clusters. This ML potential accurately covers the whole configuration space of Au clusters in a broad size range, thus expressing a good performance in search of their global minimum energy structures. Based on our potential, the low-lying structures of 17 different sized Au clusters are identified, which shows that small sized Au clusters tend to form planar structures while large ones are more likely to be stereo, revealing the critical size for the two-dimensional (2D) to three-dimensional (3D) structural transition. Our calculations demonstrate that ML is indeed powerful in describing the interaction of Au atoms and provides a new paradigm on accelerating the search of structures.

关键词: machine learning potential, gold cluster, first-principles calculation

Abstract: A machine learning (ML) potential for Au clusters is developed through training on a dataset including several different sized clusters. This ML potential accurately covers the whole configuration space of Au clusters in a broad size range, thus expressing a good performance in search of their global minimum energy structures. Based on our potential, the low-lying structures of 17 different sized Au clusters are identified, which shows that small sized Au clusters tend to form planar structures while large ones are more likely to be stereo, revealing the critical size for the two-dimensional (2D) to three-dimensional (3D) structural transition. Our calculations demonstrate that ML is indeed powerful in describing the interaction of Au atoms and provides a new paradigm on accelerating the search of structures.

Key words: machine learning potential, gold cluster, first-principles calculation

中图分类号: (Neural networks)

84.35.+i

34.20.Cf (Interatomic potentials and forces) 36.40.-c (Atomic and molecular clusters) 73.61.At (Metal and metallic alloys)

Tonghe Ying(应通和), Jianbao Zhu(朱健保), and Wenguang Zhu(朱文光). Machine learning potential aided structure search for low-lying candidates of Au clusters[J]. 中国物理B, 2022, 31(7): 78402-078402.

Tonghe Ying(应通和), Jianbao Zhu(朱健保), and Wenguang Zhu(朱文光). Machine learning potential aided structure search for low-lying candidates of Au clusters[J]. Chin. Phys. B, 2022, 31(7): 78402-078402.

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Machine learning potential aided structure search for low-lying candidates of Au clusters

Machine learning potential aided structure search for low-lying candidates of Au clusters

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