Electrocatalysis Informatics Driven Design of Formic Acid‐Producing Alloy Catalysts for CO 2 Electroreduction

Machine learning (ML)‐enabled high‐throughput screening to predict potential electrocatalysts for the CO 2 reduction reaction (CO 2 RR) offers new insights for energy conversion and environmental remediation. In this work, for the first time, we established a comprehensive electrocatalytic database containing ≈400 entries of CO 2 RR catalysts. Through decision tree analysis, correlation heatmaps, and feature importance ranking, we systematically decoded structure‐property relationships. Among the tested algorithms, the nonlinear tree‐ensemble method Random Forest Regression demonstrated superior predictive performance for CO 2 RR systems. Subsequent screening of 500 000 catalyst configurations generated by the the sequential model‐based algorithm configuration method, using Expected Improvement as the evaluation metric, identified promising multinary alloy catalysts for C1 molecule production. Notably, BiSb‐based alloys emerged as high‐potential candidates for CO 2 RR applications. This ML‐driven paradigm highlights the growing significance of artificial intelligence in materials discovery, synergistically combining screening efficiency, prediction accuracy, and proficiency in big data processing.