Chen Anjie, Sun Jinxin, Guan Junming, Liu Yaqi, Han Ying, Zhou Wenqi, Zhao Xinli, Wang Yanbiao, Liu Yongjun, Zhang Xiuyun
College of Physics Science and Technology, Yangzhou University, Yangzhou 225002, China.
Department of Fundamental Courses, Wuxi Institute of Technology, Wuxi 214121, China.
Nanoscale. 2024 Sep 19;16(36):16990-16997. doi: 10.1039/d4nr02112f.
Understanding the structure-performance relationship is crucial for designing highly active electrocatalysts, yet this remains a challenge. Using MoS supported metal-nonmetal atom pairs (XTM@MoS, 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 (_X and _X) dominate the activity, and their higher values usually lead to better activity. In addition, four promising candidates, , PCr@MoS, SV@MoS, SeTi@MoS, and SeSc@MoS, 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.
理解结构-性能关系对于设计高活性电催化剂至关重要，但这仍然是一个挑战。以用于析氢反应（HER）的MoS负载的金属-非金属原子对（XTM@MoS，TM = Sc-Ni，X = B、C、N、O、P、Se、Te和S）为例，我们借助密度泛函理论（DFT）计算和集成机器学习（ML）方法成功揭示了结构-活性关系。使用基于随机森林回归的ML模型来预测活性，训练后的模型表现出优异的性能且误差极小。SHapley加性解释分析表明，X原子的原子质量和共价半径（_X和_X）主导活性，其值越高通常导致活性越好。此外，还筛选出了四个具有优异活性的有前景的候选物，即PCr@MoS、SV@MoS、SeTi@MoS和SeSc@MoS。这项工作为HER提供了几种有前景的催化剂，但更重要的是，提供了一种探索结构-活性关系的工作流程。
