Harnessing machine learning for electrochemical CO2 reduction: current progress and future perspectives

Electrochemical carbon dioxide reduction (CO 2 RR) is regarded as a promising strategy for achieving sustainable carbon utilization, but the complexity and multi-scale characteristics of this reaction process pose significant challenges for the rational design of catalysts.The integration of machine learning (ML) with electrocatalysis can establish cross-scale connections among atomic structure, dynamic interfacial microenvironments, and macroscopic catalytic performance, thereby enabling predictive analysis of catalyst activity, product selectivity, reaction pathways, and electrochemical environment regulation.This outlook integrates recent advances in ML-assisted CO 2 RR, highlights the key challenges in data quality, feature interpretability, and multiscale integration, and proposes future directions for combining physics-informed modeling with realistic electrochemical reaction environments.With advances in ML, automated experimentation, and multi-source data integration, a self-evolving catalyst design platform is expected to be established.