Anjie Chen,‡ a Jinxin Sun,‡ a Junming Guan,‡ a Yaqi Liu, a Ying Han, a Wenqi Zhou, a Xinli Zhao, a Yanbiao Wang,* b Yongjun Liu* a and Xiuyun Zhang * ac
a College of Physics Science and Technology, Yangzhou University, Yangzhou 225002, China
E-mail: yjliu@yzu.edu.cn, xyzhang@yzu.edu.cn
b Department of Fundamental Courses, Wuxi Institute of Technology, Wuxi 214121, China
E-mail: wangyb@wxit.edu.cn
c Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing, China
Understanding the structure–performance relationship is crucial for designing highly active electrocatalysts, yet this remains a challenge. Using MoS2 supported metal–nonmetal atom pairs (XTM@MoS2, TM = Sc–Ni, and X = B, C, N, O, P, Se, Te, and S) for the hydrogen evolution reaction (HER) as an example, we successfully uncovered the structure–activity relationship with the help of density functional theory (DFT) calculations and integrated machine learning (ML) methods. An ML model based on random forest regression was used to predict the activity, and the trained model exhibited excellent performance with minimal error. SHapley Additive exPlanations analysis revealed that the atom mass and covalent radius of the X atom (m_X and R_X) dominate the activity, and their higher values usually lead to better activity. In addition, four promising candidates, i.e., PCr@MoS2, SV@MoS2, SeTi@MoS2, and SeSc@MoS2, with excellent activity are selected. This work provides several promising catalysts for the HER but, more importantly, offers a workflow to explore the structure–activity relationship.