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These challenges become more difficult in dual\u2010site photocatalysts, where metal combinations, coordination structures, intersite distances, support effects, and reaction conditions jointly influence CO\n                    <jats:sub>2<\/jats:sub>\n                    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\u2010learning\u2010assisted design of dual\u2010site photocatalysts for selective CO\n                    <jats:sub>2<\/jats:sub>\n                    to C\n                    <jats:sub>2<\/jats:sub>\n                    + conversion. We first discuss the reaction network of photocatalytic CO\n                    <jats:sub>2<\/jats:sub>\n                    reduction, especially proton\u2010coupled electron transfer and C\u2500C coupling pathways involving\n                    <jats:sup>*<\/jats:sup>\n                    CO,\n                    <jats:sup>*<\/jats:sup>\n                    CHO,\n                    <jats:sup>*<\/jats:sup>\n                    OCCO, and\n                    <jats:sup>*<\/jats:sup>\n                    CH\n                    <jats:sub>3<\/jats:sub>\n                    intermediates. We then classify representative dual\u2010site systems, including dual\u2010atom catalysts, single atom\u2013nanocluster\/nanoparticle composites, and alloy\/interface\u2010based catalysts. Particular attention is given to how machine learning supports descriptor identification, adsorption\u2010energy prediction, catalyst screening, and reaction\u2010pathway analysis, providing useful guidance for developing pathway\u2010oriented dual\u2010site photocatalysts.\n                  <\/jats:p>","DOI":"10.1002\/smll.75400","type":"journal-article","created":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T09:35:54Z","timestamp":1787304954000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual\u2010Site Photocatalysts for Selective CO\n                    <sub>2<\/sub>\n                    to C\n                    <sub>2<\/sub>\n                    + Conversion: Pathway Regulation and Machine Learning Guided Design"],"prefix":"10.1002","author":[{"given":"Yicheng","family":"Li","sequence":"first","affiliation":[{"name":"School of Materials Science and Engineering National Institute for Advanced 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