ECTS: An ultra-fast diffusion model for exploring chemical reactions with equivariant consistency

ECTS: An ultra-fast diffusion model for exploring chemical reactions with equivariant consistency | 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 ECTS: An ultra-fast diffusion model for exploring chemical reactions with equivariant consistency Tong Zhu, Mingyuan Xu, Bowen Li, Zhaojia Dong, Pavlo Dral, Hongming Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6956918/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Unveiling reaction mechanisms through the exploration of reaction paths — including the identification of transition states (TS), prediction of activation barriers (Eₐ), and mapping of reaction pathways — is central to the study of chemical reactions. However, this process typically relies on extensive and computationally intensive quantum chemical calculations. In this work, we present an Equivariant Consistency generative model for Transition States (ECTS), an ultra-fast diffusion framework that seamlessly integrates TS generation, energy prediction, and pathway exploration within a unified architecture. ECTS demonstrates a computational efficiency improvement of at least two orders of magnitude over conventional diffusion-based models. The generated TS structures exhibit excellent accuracy, with a root-mean-square deviation (RMSD) of only 0.12Å from reference geometries. Furthermore, by progressively refining energy barrier predictions during the denoising process, ECTS achieves a median error of just 2.4 kcal/mol, without requiring any post-DFT corrections. Notably, ECTS also produces reaction pathways that are generally consistent with true reaction paths, demonstrating its potential as a powerful tool for reaction mechanism exploration. Physical sciences/Chemistry/Theoretical chemistry/Computational chemistry Physical sciences/Chemistry/Physical chemistry Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SI.pdf Supporting Information machinelearningchecklist.pdf Machine Learning Checklist nrsoftwarepolicy.pdf Software Policy Checklist Cite Share Download PDF Status: Posted 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-6956918","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":489710721,"identity":"b3db6711-63e8-47ba-a716-c02544718fae","order_by":0,"name":"Tong Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYFACHgaGDwUILmMDYQ08DIwzDEjVwsxDkhZ7/rPHpG0M7tgzsDc/e8zDYCO74QDzswf4bTmXJp1j8CyxgeeYuTEPQ5rxhgNs5gZ4tTD2mAG1HE5gkMhhk+ZhOJy44QAPmwReLcw8ZtIWBoftGeTfgLT8J0ILG1ALg8FhxgYJHpCWA0RoOcNjbNkD9EsbT5qZ5ByDZOOZh9nM8Gph7z9jeONHxR17fvbDzyTeVNjJ9h1vfoZXCxQcYGAD06CgYiZCPVjLKBgFo2AUjAKcAADNCDru5hut3AAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-7472-3736","institution":"East China Normal University","correspondingAuthor":true,"prefix":"","firstName":"Tong","middleName":"","lastName":"Zhu","suffix":""},{"id":489710722,"identity":"ac7e28e0-e83a-41d2-b99d-cc8a20785022","order_by":1,"name":"Mingyuan Xu","email":"","orcid":"https://orcid.org/0009-0006-9397-5174","institution":"Guangzhou Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Mingyuan","middleName":"","lastName":"Xu","suffix":""},{"id":489710723,"identity":"e12169fd-c00c-4295-b506-7bb6d160d327","order_by":2,"name":"Bowen Li","email":"","orcid":"","institution":"East China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Bowen","middleName":"","lastName":"Li","suffix":""},{"id":489710724,"identity":"e8a31311-4395-40ee-a9d3-80366fa2a6ef","order_by":3,"name":"Zhaojia Dong","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Zhaojia","middleName":"","lastName":"Dong","suffix":""},{"id":489710725,"identity":"8e2335a7-2a15-4449-888a-fec5d4587996","order_by":4,"name":"Pavlo Dral","email":"","orcid":"https://orcid.org/0000-0002-2975-9876","institution":"Xiamen University","correspondingAuthor":false,"prefix":"","firstName":"Pavlo","middleName":"","lastName":"Dral","suffix":""},{"id":489710726,"identity":"b2c0b9f2-c4b9-4d30-80a1-f13261a7037f","order_by":5,"name":"Hongming Chen","email":"","orcid":"https://orcid.org/0000-0002-8065-8333","institution":"Guangzhou National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Hongming","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-06-23 12:40:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6956918/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6956918/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89844038,"identity":"5ebf2914-7197-481e-8713-436f219319f8","added_by":"auto","created_at":"2025-08-25 15:48:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1197061,"visible":true,"origin":"","legend":"Article File #1","description":"","filename":"Manuscript20250704.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6956918/v1_covered_4bf9c652-6af8-4436-956a-48c970409fe4.pdf"},{"id":87460384,"identity":"24f5e768-cf0d-41e4-8af1-fdc5d420f904","added_by":"auto","created_at":"2025-07-24 05:46:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":754002,"visible":true,"origin":"","legend":"Supporting Information","description":"","filename":"SI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6956918/v1/11c8304c9054081fccbc09d3.pdf"},{"id":87460381,"identity":"dbe66afe-d4ad-480c-84ae-0b84b919281c","added_by":"auto","created_at":"2025-07-24 05:46:32","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":355695,"visible":true,"origin":"","legend":"Machine Learning Checklist","description":"","filename":"machinelearningchecklist.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6956918/v1/5cdf15bebddcd82177c24acd.pdf"},{"id":87460385,"identity":"5093d23b-283c-48e9-bdc5-c98ebf1e6a95","added_by":"auto","created_at":"2025-07-24 05:46:32","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1317245,"visible":true,"origin":"","legend":"Software Policy Checklist","description":"","filename":"nrsoftwarepolicy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6956918/v1/9002a351900134e966c03588.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"ECTS: An ultra-fast diffusion model for exploring chemical reactions with equivariant consistency","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6956918/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6956918/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Unveiling reaction mechanisms through the exploration of reaction paths — including the identification of transition states (TS), prediction of activation barriers (Eₐ), and mapping of reaction pathways — is central to the study of chemical reactions. However, this process typically relies on extensive and computationally intensive quantum chemical calculations. In this work, we present an Equivariant Consistency generative model for Transition States (ECTS), an ultra-fast diffusion framework that seamlessly integrates TS generation, energy prediction, and pathway exploration within a unified architecture. ECTS demonstrates a computational efficiency improvement of at least two orders of magnitude over conventional diffusion-based models. The generated TS structures exhibit excellent accuracy, with a root-mean-square deviation (RMSD) of only 0.12Å from reference geometries. Furthermore, by progressively refining energy barrier predictions during the denoising process, ECTS achieves a median error of just 2.4 kcal/mol, without requiring any post-DFT corrections. Notably, ECTS also produces reaction pathways that are generally consistent with true reaction paths, demonstrating its potential as a powerful tool for reaction mechanism exploration.","manuscriptTitle":"ECTS: An ultra-fast diffusion model for exploring chemical reactions with equivariant consistency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-24 05:46:27","doi":"10.21203/rs.3.rs-6956918/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f45aa92f-42a9-43d4-9dd8-aa83cdf9a151","owner":[],"postedDate":"July 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51991942,"name":"Physical sciences/Chemistry/Theoretical chemistry/Computational chemistry"},{"id":51991943,"name":"Physical sciences/Chemistry/Physical chemistry"}],"tags":[],"updatedAt":"2025-08-25T15:40:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-24 05:46:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6956918","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6956918","identity":"rs-6956918","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}