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Herein, density functional theory combined with interpretable machine learning were employed to investigate the structure\u2010activity relationship of Cu\u2010based single\u2010atom alloys constructed by incorporating 13 transition\u2010metal atoms into Cu(100)\/(111) surfaces. The calculations reveal that isolated metal atoms effectively regulate the electronic structures of both the dopant and neighboring Cu atoms, thereby altering the energetics of HCOO* formation. More importantly, the synergistic interaction between the isolated metal atom and adjacent Cu sites stabilizes the CH\n                    <jats:sub>2<\/jats:sub>\n                    OO*, redirecting the reaction pathway toward highly selective CH\n                    <jats:sub>4<\/jats:sub>\n                    , in sharp contrast to pristine Cu surfaces. Among all investigated catalysts, Ge\n                    <jats:sub>1<\/jats:sub>\n                    \/Cu(100) is most active, with a limiting free energy as low as 0.24\u00a0eV while effectively suppressing the hydrogen evolution reaction. Machine learning analysis further identifies electron affinity and catalyst\u2010support charge transfer as the two dominant descriptors governing catalytic activity, providing quantitative insight into the origin of the structure\u2010activity relationship. This combined DFT\u2010ML framework establishes descriptor\u2010guided design principles for methane\u2010selective CO\n                    <jats:sub>2<\/jats:sub>\n                    reduction and offers a general strategy for the rational development of high\u2010performance single\u2010atom alloy electrocatalysts.\n                  <\/jats:p>","DOI":"10.1002\/chem.71784","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T07:34:50Z","timestamp":1791617690000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unveiling Methane Selectivity on Cu\u2010Based Single\u2010Atom Alloys for CO\n                    <sub>2<\/sub>\n                    Electroreduction via Synergistic DFT and Machine Learning"],"prefix":"10.1002","author":[{"given":"Bicheng","family":"Zhang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Flexible Electronics (LoFE) &amp; Institute of Advanced Materials (IAM) Nanjing University of Posts and Telecommunications  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiawen","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Flexible Electronics (LoFE) &amp; Institute of Advanced Materials (IAM) Nanjing University of Posts and Telecommunications  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Flexible Electronics (LoFE) &amp; Institute of Advanced Materials (IAM) Nanjing University of Posts and Telecommunications  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Flexible Electronics (LoFE) &amp; Institute of Advanced Materials (IAM) Nanjing University of Posts and Telecommunications  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8618-2751","authenticated-orcid":false,"given":"Xiuyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Physics Science and Technology Yangzhou University  Yangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1555-1396","authenticated-orcid":false,"given":"Yixin","family":"Ouyang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Flexible Electronics (LoFE) &amp; Institute of Advanced Materials (IAM) Nanjing University of Posts and Telecommunications  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2265-1037","authenticated-orcid":false,"given":"Li","family":"Shi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Flexible Electronics (LoFE) &amp; Institute of Advanced Materials (IAM) Nanjing University of Posts and Telecommunications  Nanjing China"},{"name":"Key Laboratory of Quantum Materials and Devices of Ministry of Education School of Physics Southeast University  Nanjing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,10]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1007\/s40843-022-2222-6","article-title":"Theoretical Studies of Non\u2010Noble Metal Single\u2010Atom Catalyst Ni1\/MoS2: Electronic Structure and Electrocatalytic CO2 Reduction","volume":"66","author":"Yu Q.","year":"2023","journal-title":"Science China Materials"},{"key":"e_1_2_7_3_1","doi-asserted-by":"crossref","first-page":"3423","DOI":"10.1002\/adma.201504766","article-title":"Recent Advances in Inorganic Heterogeneous Electrocatalysts for Reduction of Carbon Dioxide","volume":"28","author":"Zhu D. 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