Unlocking the Potential of Machine Learning for Advanced Single‐Atom Catalysts Design in Promoting Electrochemical CO ₂ Reduction

ABSTRACT

Electrocatalytic CO ₂ reduction reaction (CO ₂ RR) represents a pivotal pathway for the promotion of carbon neutrality and the preparation of highly valuable chemicals. To improve the product selectivity and catalytic efficiency of CO ₂ RR, single‐atom catalysts (SACs) offering highly efficient atomic utilization rates and adjustable electronic properties attract substantial attention. However, SAC exploration in the vast parameter space through traditional trial‐and‐error methods is greatly impeded by long periods and high costs. Fortunately, machine learning (ML) serves as a powerful data‐driven tool to promote the development of SACs by extracting complex structure–performance correlations in huge datasets. Herein, ML applications in the exploration of SACs for CO ₂ RR are reviewed, emphasizing a comprehensive workflow extending from data collection and feature engineering to model selection and rigorous validation. Moreover, achievements in ML‐guided materials screening, informative descriptor extraction, rational design of SACs, and mechanistic elucidation are highlighted. Finally, future developments integrating high‐throughput (HT) synthesis, advanced characterization, multi‐source databases, and interpretable ML are proposed to facilitate knowledge‐based development of SACs with optimized CO ₂ RR performance. This work aims to develop a closed‐loop workflow for the rational design of highly active and selective CO ₂ RR SACs, providing novel paradigms for both fundamental research and practical application of electrocatalysts.