Accelerating the search for carbon cluster isomers via machine-learning potential

Accelerating the search for carbon cluster isomers via machine-learning potential | 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 Research Article Accelerating the search for carbon cluster isomers via machine-learning potential Huy Duy Nguyen, Phong Hai Nguyen, Giang Huong Bach, Oanh Kim Thi Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7540372/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 Cage-like isomers of C22 clusters are explored with machine-learning potential GAP-20 and density-functional theory (DFT) calculations. Using the GAP-20 to approximate the energy landscape helps reduce the search time and provides superior starting structures for subsequent DFT geometry optimization. However, the Jahn-Teller distortion is not predicted by the GAP-20. The relative GAP-20 energies are overestimated, and the vibrational modes/frequencies are poorly characterized. The thermodynamic and chemical stabilities of the cages are discussed, and the simulated infrared spectra are provided. Carbon clusters low-energy isomers machine-learning potential Full Text Additional Declarations No competing interests reported. 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-7540372","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514830998,"identity":"e6cf458e-058f-43d2-9c9a-749e523d4cf9","order_by":0,"name":"Huy Duy Nguyen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYBACAyCWYKg4AOZIkKDlDMlaGNtI0WIukWN4u3DencT5DcwHb/MwHM4jqMVyRo6x9cxtzxI3HGBLtgZqKSbssBs5ZtK82w4nbmDgMZPmYUhLbCBOy5zDQIfxfyNFS8PhxIYDPGxALTZEaDnzrNia59hh4w2H2Ywt5xgQo+V48sbbPDWHZee3Nz+88aZCgrAWBgYOAwjNDDaBsHogYH9AlLJRMApGwSgYwQAAHWE7kutGnwIAAAAASUVORK5CYII=","orcid":"","institution":"VNU University of Science","correspondingAuthor":true,"prefix":"","firstName":"Huy","middleName":"Duy","lastName":"Nguyen","suffix":""},{"id":514830999,"identity":"d2c57013-ec03-461b-8344-ed6285724f27","order_by":1,"name":"Phong Hai Nguyen","email":"","orcid":"","institution":"VNU University of Science","correspondingAuthor":false,"prefix":"","firstName":"Phong","middleName":"Hai","lastName":"Nguyen","suffix":""},{"id":514831000,"identity":"1bb5df60-cdd9-4763-be03-c00927224d5e","order_by":2,"name":"Giang Huong Bach","email":"","orcid":"","institution":"VNU University of Science","correspondingAuthor":false,"prefix":"","firstName":"Giang","middleName":"Huong","lastName":"Bach","suffix":""},{"id":514831002,"identity":"b2343388-a9bc-498e-bddb-2e919dbb6603","order_by":3,"name":"Oanh Kim Thi Nguyen","email":"","orcid":"","institution":"Electric Power University","correspondingAuthor":false,"prefix":"","firstName":"Oanh","middleName":"Kim Thi","lastName":"Nguyen","suffix":""}],"badges":[],"createdAt":"2025-09-05 03:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7540372/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7540372/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92306745,"identity":"2a1cfad7-bb4a-4bf4-a223-31096cad6024","added_by":"auto","created_at":"2025-09-27 07:53:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":739747,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7540372/v1_covered_18b34c56-801b-4766-8e89-0721463e800f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Accelerating the search for carbon cluster isomers via machine-learning potential","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Carbon clusters, low-energy isomers, machine-learning potential","lastPublishedDoi":"10.21203/rs.3.rs-7540372/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7540372/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cage-like isomers of C22 clusters are explored with machine-learning potential GAP-20 and density-functional theory (DFT) calculations. Using the GAP-20 to approximate the energy landscape helps reduce the search time and provides superior starting structures for subsequent DFT geometry optimization. However, the Jahn-Teller distortion is not predicted by the GAP-20. The relative GAP-20 energies are overestimated, and the vibrational modes/frequencies are poorly characterized. The thermodynamic and chemical stabilities of the cages are discussed, and the simulated infrared spectra are provided.","manuscriptTitle":"Accelerating the search for carbon cluster isomers via machine-learning potential","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-16 03:04:57","doi":"10.21203/rs.3.rs-7540372/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":"e3b8873d-0a99-4474-8995-6383d668c9b2","owner":[],"postedDate":"September 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-12T04:38:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-16 03:04:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7540372","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7540372","identity":"rs-7540372","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}