Dual-Site Photocatalysts for Selective CO₂ to C₂+ Conversion: Pathway Regulation and Machine Learning Guided Design

Photocatalytic CO₂ reduction to C₂+ 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 C₁ 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 CO₂ 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 CO₂ to C₂+ conversion. We first discuss the reaction network of photocatalytic CO₂ reduction, especially proton-coupled electron transfer and C─C coupling pathways involving ^*CO, ^*CHO, ^*OCCO, and ^*CH₃ 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.