From Atoms to Dynamics: Learning the Committor Without Collective Variables

From Atoms to Dynamics: Learning the Committor Without Collective Variables | 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 Brief Communication From Atoms to Dynamics: Learning the Committor Without Collective Variables Christophe Chipot, Sergio Contreras Arredondo, Chenyu Tang, Radu Talmazan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7295644/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Feb, 2026 Read the published version in Nature Computational Science → Version 1 posted You are reading this latest preprint version Abstract This Brief Communication introduces a graph-neural-network architecture built on geometric vector perceptrons to predict the committor function directly from atomic coordinates, bypassing the need for hand-crafted collective variables (CVs). The method offers atom-level interpretability, pinpointing the key atomic players in complex transitions without relying on prior assumptions. Applied across diverse molecular systems, the method accurately infers the committor function and highlights the importance of each heavy atom in the transition mechanism. It also yields precise estimates of the rate constants for the underlying processes. The proposed approach opens new avenues for understanding and modeling complex dynamics, by enabling CV-free learning and automated identification of physically meaningful reaction coordinates of complex molecular processes. Physical sciences/Physics/Biological physics Physical sciences/Chemistry/Theoretical chemistry/Statistical mechanics Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SI.pdf Supplementary Information Cite Share Download PDF Status: Published Journal Publication published 17 Feb, 2026 Read the published version in Nature Computational Science → Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7295644","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Brief Communication","associatedPublications":[],"authors":[{"id":505430481,"identity":"cda55d56-fab9-45a1-84de-29d9ed307f0f","order_by":0,"name":"Christophe Chipot","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDACCcYGIGnDw8cOYhiAhBKI0ZKQxsPGTLwWsKLDDGzMcCECWvhnN7c9+PjjvAwbM3Pbgw8FdQz87DkG+C25c7DdcEbCbZDDgAyDwwySPW/wazGQSGyT5oFoATIMDjAY3CBgC1TLOZiWOgZ7IrUcgGlhBooQ8suNRKAX0pLBWiSBfuGROPOsAK8W/hnpzx58sLGz52dvfybx4U+dHH978ga8WoCADYXHQ0g5ppZRMApGwSgYBRgAAIh3OiWEVScvAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-9122-1698","institution":"Université de Lorraine","correspondingAuthor":true,"prefix":"","firstName":"Christophe","middleName":"","lastName":"Chipot","suffix":""},{"id":505430482,"identity":"ec610a7f-6fa1-4db4-832b-072e6d4d4805","order_by":1,"name":"Sergio Contreras Arredondo","email":"","orcid":"https://orcid.org/0009-0006-0428-4962","institution":"Université de Lorraine","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"Contreras","lastName":"Arredondo","suffix":""},{"id":505430483,"identity":"f436f2cd-b8b9-47cf-9996-696fac4df1b2","order_by":2,"name":"Chenyu Tang","email":"","orcid":"https://orcid.org/0000-0002-6914-7348","institution":"Université de Lorraine","correspondingAuthor":false,"prefix":"","firstName":"Chenyu","middleName":"","lastName":"Tang","suffix":""},{"id":505430484,"identity":"59ae00eb-7526-4b83-92e7-cf6d86b1eadc","order_by":3,"name":"Radu Talmazan","email":"","orcid":"https://orcid.org/0000-0001-6678-7801","institution":"University of Lorraine","correspondingAuthor":false,"prefix":"","firstName":"Radu","middleName":"","lastName":"Talmazan","suffix":""},{"id":505430485,"identity":"b5a0f9c2-c5fc-4807-85c8-7f42e2b4f8aa","order_by":4,"name":"Alberto Megías","email":"","orcid":"https://orcid.org/0000-0002-7889-1312","institution":"Université de Lorraine","correspondingAuthor":false,"prefix":"","firstName":"Alberto","middleName":"","lastName":"Megías","suffix":""},{"id":505430486,"identity":"df99c471-c351-4ebc-bab9-a966bd2cc82b","order_by":5,"name":"Cheng Giuseppe Chen","email":"","orcid":"https://orcid.org/0000-0003-3553-4718","institution":"Université de Lorraine","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"Giuseppe","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-08-05 02:55:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7295644/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7295644/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43588-026-00958-2","type":"published","date":"2026-02-17T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102901150,"identity":"f198afe3-cd92-4cd5-90e6-8d75f9c36623","added_by":"auto","created_at":"2026-02-18 08:11:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3061344,"visible":true,"origin":"","legend":"Article File","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7295644/v1_covered_0e165bf0-b9a7-4c15-b808-71514f28cc6b.pdf"},{"id":89916731,"identity":"803ed8d6-3ce1-43d5-8611-55a3b16dfe84","added_by":"auto","created_at":"2025-08-26 11:58:07","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6030703,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7295644/v1/40c4ce59a2ccbb6ac9560a87.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"From Atoms to Dynamics: Learning the Committor Without Collective Variables","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7295644/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7295644/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This Brief Communication introduces a graph-neural-network architecture built on geometric vector perceptrons to predict the committor function directly from atomic coordinates, bypassing the need for hand-crafted collective variables (CVs). The method offers atom-level interpretability, pinpointing the key atomic players in complex transitions without relying on prior assumptions. Applied across diverse molecular systems, the method accurately infers the committor function and highlights the importance of each heavy atom in the transition mechanism. It also yields precise estimates of the rate constants for the underlying processes. The proposed approach opens new avenues for understanding and modeling complex dynamics, by enabling CV-free learning and automated identification of physically meaningful reaction coordinates of complex molecular processes.","manuscriptTitle":"From Atoms to Dynamics: Learning the Committor Without Collective Variables","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-26 11:58:02","doi":"10.21203/rs.3.rs-7295644/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-computational-science","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"natcomputsci","sideBox":"Learn more about [Nature Computational Science](http://www.nature.com/natcomputsci/)","snPcode":"","submissionUrl":"","title":"Nature Computational Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"321a4a4c-c952-4a43-967e-5fd9152297e3","owner":[],"postedDate":"August 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53684597,"name":"Physical sciences/Physics/Biological physics"},{"id":53684598,"name":"Physical sciences/Chemistry/Theoretical chemistry/Statistical mechanics"}],"tags":[],"updatedAt":"2026-02-18T08:10:37+00:00","versionOfRecord":{"articleIdentity":"rs-7295644","link":"https://doi.org/10.1038/s43588-026-00958-2","journal":{"identity":"nature-computational-science","isVorOnly":false,"title":"Nature Computational Science"},"publishedOn":"2026-02-17 05:00:00","publishedOnDateReadable":"February 17th, 2026"},"versionCreatedAt":"2025-08-26 11:58:02","video":"","vorDoi":"10.1038/s43588-026-00958-2","vorDoiUrl":"https://doi.org/10.1038/s43588-026-00958-2","workflowStages":[]},"version":"v1","identity":"rs-7295644","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7295644","identity":"rs-7295644","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}