Dual‐Site Photocatalysts for Selective CO 2 to C 2 + Conversion: Pathway Regulation and Machine Learning Guided Design

Photocatalytic CO 2 reduction to C 2 + products is an attractive route for solar to chemical conversion, yet its development is still limited by inefficient charge utilization, sluggish C─C coupling, competing C 1 pathways, and the complex adsorption behavior of carbonaceous intermediates. These challenges become more difficult in dual‐site photocatalysts, where metal combinations, coordination structures, intersite distances, support effects, and reaction conditions jointly influence CO 2 activation, intermediate evolution, and product selectivity. Machine learning offers a practical way to deal with this complexity by connecting catalyst structures with key descriptors and catalytic performance. In this review, we summarize recent progress in machine‐learning‐assisted design of dual‐site photocatalysts for selective CO 2 to C 2 + conversion. We first discuss the reaction network of photocatalytic CO 2 reduction, especially proton‐coupled electron transfer and C─C coupling pathways involving * CO, * CHO, * OCCO, and * CH 3 intermediates. We then classify representative dual‐site systems, including dual‐atom catalysts, single atom–nanocluster/nanoparticle composites, and alloy/interface‐based catalysts. Particular attention is given to how machine learning supports descriptor identification, adsorption‐energy prediction, catalyst screening, and reaction‐pathway analysis, providing useful guidance for developing pathway‐oriented dual‐site photocatalysts.