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Understanding the correlation between the fundamental descriptors and catalytic performance is meaningful to guide the design of high‐performance electrochemical catalysts. However, exploring key factors that affect catalytic performance in the vast catalyst space remains challenging for people. Herein, to accurately identify the factors that affect the performance of N2 reduction, we apply interpretable machine learning (ML) to analyze high‐throughput screening results, which is also suited to other surface reactions in catalysis. To expound on the paradigm, 33 promising catalysts are screened from 168 carbon‐supported candidates, specifically single‐atom catalysts (SACs) supported by a BC3 monolayer (TM@VB/C‐Nn = 0–3‐BC3) via high‐throughput screening. Subsequently, the hybrid sampling method and XGBoost model are selected to classify eligible and non‐eligible catalysts. Through feature interpretation using Shapley Additive Explanations (SHAP) analysis, two crucial features, that is, the number of valence electrons (Nv) and nitrogen substitution (Nn), are screened out. Combining SHAP analysis and electronic structure calculations, the synergistic effect between an active center with low valence electron numbers and reasonable C‐N coordination (a medium fraction of nitrogen substitution) can exhibit high catalytic performance. Finally, six superior catalysts with a limiting potential lower than −0.4 V are predicted. Our workflow offers a rational approach to obtaining key information on catalytic performance from high‐throughput screening results to design efficient catalysts that can be applied to other materials and reactions. [ABSTRACT FROM AUTHOR]

Understanding the structure-performance relationship is crucial for designing highly active electrocatalysts, yet this remains a challenge. Using MoS 2 supported metal-nonmetal atom pairs (XTM@MoS 2 , 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@MoS 2 , SV@MoS 2 , SeTi@MoS 2 , and SeSc@MoS 2 , 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.