Electronic and Geometric Effects of Dual-Atom Catalysts on C−C Coupling during CO2 Reduction: Insights from Density Functional Theory and Interpretable Machine Learning

Electronic and Geometric Effects of Dual-Atom Catalysts on C−C Coupling during CO2 Reduction: Insights from Density Functional Theory and Interpretable Machine Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Electronic and Geometric Effects of Dual-Atom Catalysts on C−C Coupling during CO 2 Reduction: Insights from Density Functional Theory and Interpretable Machine Learning Chenglong Qiu, Tore Brinck, Jiacheng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8914167/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Dual-atom catalysts (DACs) offer unique opportunities for promoting C 2+ formation in CO 2 electroreduction; however, the roles of electronic and geometric structures in governing C − C coupling remain unclear. Herein, we systematically investigate eighty fourth-row transition-metal dimers anchored on N-doped graphene using density functional theory combined with interpretable machine learning. Our results demonstrate that both *CO and *OCCO adsorption strongly depend on the metal identity and metal–metal distance, exhibiting volcano-type or linear relationships that lead to regular variations in C − C coupling energies. To establish quantitative relationships between coupling energy and the electronic and geometric structures, we construct interpretable machine-learning models based on physically motivated descriptors. Feature screening and SISSO analysis identify the number of metal valence electrons and the metal−metal distance as the dominant factors governing coupling energies. The resulting three-dimensional descriptor achieves good predictive performance, enabling rapid evaluation of DAC activity for the C − C coupling process. Overall, this study provides fundamental insights into the structure-activity relationships of DACs and offers a practical strategy for designing catalysts with enhanced C 2+ selectivity. Physical sciences/Chemistry Physical sciences/Materials science C − C coupling Dual-atom catalysts Structure-activity relationship Density functional theory Interpretable machine learning Full Text Additional Declarations No competing interests reported. Supplementary Files SI.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 19 Mar, 2026 Reviewers invited by journal 17 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 11 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8914167","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":609103117,"identity":"4fcfa918-30d2-4640-aa94-6e7fb338bb84","order_by":0,"name":"Chenglong Qiu","email":"","orcid":"","institution":"Taizhou University","correspondingAuthor":false,"prefix":"","firstName":"Chenglong","middleName":"","lastName":"Qiu","suffix":""},{"id":609103118,"identity":"19b60e95-bc08-439e-a38a-b070e6e4571d","order_by":1,"name":"Tore Brinck","email":"","orcid":"","institution":"KTH Royal Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Tore","middleName":"","lastName":"Brinck","suffix":""},{"id":609103120,"identity":"254fc9db-fe08-4c07-8e0f-4be87a135397","order_by":2,"name":"Jiacheng Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBACxmYGBmYEtwJCSZCg5YwBYS0ggNDC2EaEFuZ25oefCyru2DVIJD97+HXeH3v+BuaDt3kY7PJwO4zNWHrGmWfJDRJp5say2wyYJQ6wJVvzMCQX4/GLGTNv2+FkBukEM2nJbQZsBgw8ZtI8DAcSG3BqYf/GzPsPpCX9m7TkHAMeAwb+bwS08ABtaThsxyCdYyb5scFAAmgLGyEtxdI8xw4nsMm/KZNmOGZsIHGYzdhyjkEyTi2G/cc3fuapOWzPz3N8m+SPGjl7/vbmhzfeVNjh1gKVSGwDEsw8ICY4mgxwqAcCeShtD3blD9wKR8EoGAWjYAQDAGnrSU4tnjx2AAAAAElFTkSuQmCC","orcid":"","institution":"Taizhou University","correspondingAuthor":true,"prefix":"","firstName":"Jiacheng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-02-19 05:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8914167/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8914167/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105035664,"identity":"aa925a8d-eb78-4eef-bd0b-bd5827c76e67","added_by":"auto","created_at":"2026-03-20 07:26:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1023025,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptrevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8914167/v1_covered_d623d304-6f83-4c3b-942d-35fcb653a1ad.pdf"},{"id":105028251,"identity":"0ef92a76-64d6-4814-965e-a4d0255f877a","added_by":"auto","created_at":"2026-03-20 05:46:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":11764840,"visible":true,"origin":"","legend":"","description":"","filename":"SI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8914167/v1/20dd4fdeb6eb9ef560c70e37.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eElectronic and Geometric Effects of Dual-Atom Catalysts on C−C Coupling during CO\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e Reduction: Insights from Density Functional Theory and Interpretable Machine Learning\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-computational-materials","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"npjcompumats","sideBox":"Learn more about [npj Computational Materials](http://www.nature.com/npjcompumats/)","snPcode":"41524","submissionUrl":"https://mts-npjcompumats.nature.com/","title":"npj Computational Materials","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"C − C coupling, Dual-atom catalysts, Structure-activity relationship, Density functional theory, Interpretable machine learning","lastPublishedDoi":"10.21203/rs.3.rs-8914167/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8914167/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDual-atom catalysts (DACs) offer unique opportunities for promoting C\u003csub\u003e2+\u003c/sub\u003e formation in CO\u003csub\u003e2\u003c/sub\u003e electroreduction; however, the roles of electronic and geometric structures in governing C\u0026thinsp;\u0026minus;\u0026thinsp;C coupling remain unclear. Herein, we systematically investigate eighty fourth-row transition-metal dimers anchored on N-doped graphene using density functional theory combined with interpretable machine learning. Our results demonstrate that both *CO and *OCCO adsorption strongly depend on the metal identity and metal\u0026ndash;metal distance, exhibiting volcano-type or linear relationships that lead to regular variations in C\u0026thinsp;\u0026minus;\u0026thinsp;C coupling energies. To establish quantitative relationships between coupling energy and the electronic and geometric structures, we construct interpretable machine-learning models based on physically motivated descriptors. Feature screening and SISSO analysis identify the number of metal valence electrons and the metal\u0026minus;metal distance as the dominant factors governing coupling energies. The resulting three-dimensional descriptor achieves good predictive performance, enabling rapid evaluation of DAC activity for the C\u0026thinsp;\u0026minus;\u0026thinsp;C coupling process. Overall, this study provides fundamental insights into the structure-activity relationships of DACs and offers a practical strategy for designing catalysts with enhanced C\u003csub\u003e2+\u003c/sub\u003e selectivity.\u003c/p\u003e","manuscriptTitle":"Electronic and Geometric Effects of Dual-Atom Catalysts on C−C Coupling during CO2 Reduction: Insights from Density Functional Theory and Interpretable Machine Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 05:46:26","doi":"10.21203/rs.3.rs-8914167/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"240875455596056401091273230222956175839","date":"2026-03-19T20:26:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-17T19:35:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-11T13:14:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Computational Materials","date":"2026-03-11T08:53:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-computational-materials","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"npjcompumats","sideBox":"Learn more about [npj Computational Materials](http://www.nature.com/npjcompumats/)","snPcode":"41524","submissionUrl":"https://mts-npjcompumats.nature.com/","title":"npj Computational Materials","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"865fc7aa-ee03-482d-a995-4fc3c197703b","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64828476,"name":"Physical sciences/Chemistry"},{"id":64828477,"name":"Physical sciences/Materials science"}],"tags":[],"updatedAt":"2026-03-20T05:46:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-20 05:46:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8914167","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8914167","identity":"rs-8914167","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}