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Abstract

Among the most active oxygen evolution reaction (OER) catalysts, mixed metal oxides based on Ni, Fe, and Co metals are recognized as economical yet excellent replacements for RuO₂ and IrO_x. However, tuning and searching for optimal compositions of multi–element–compound electrocatalysts is a big challenge in catalysis research. Conventional materials screening experiments and theoretical simulations are labor–intensive and time–consuming. Machine learning offers a promising paradigm for accelerating electrocatalyst research and simultaneously understanding composition–activity correlation. Herein, we introduce an Explainable AI (XAI) framework for predicting the electrocatalytic performance of OER catalysts. By integrating the robust Random Forest (RF) model for machine learning with the Shapley Additive Explanations (SHAP) method for model explanation, we achieved accurate predictions of the overpotential for various compositions of (Ni−Fe−Co−Ce)O_x catalysts (R²=0.8221). More importantly, we obtained valuable insights into how each metal and their interactions influence the overpotential of the catalysts. Our results highlight the versatility of the RF model with SHAP in identifying the optimal composition of (Ni−Fe−Co−Ce)O_x catalysts for electrocatalytic oxygen evolution, showing its potential applicability across various catalyst synthesis methods. Finally, we anticipate that this work will lead to exciting possibilities in designing highly active multi–element compound electrocatalysts with the aid of explainable AI.