Machine learning-aided understanding of the structure-activity relationship: a case study of MoS supported metal-nonmetal pairs for the hydrogen evolution reaction.

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.