Leveraging Machine Learning to Predict High-Temperature Superconductors

Leveraging Machine Learning to Predict High-Temperature Superconductors | 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 Leveraging Machine Learning to Predict High-Temperature Superconductors Ibrahim Nadeem This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6637156/v3 This work is licensed under a CC BY 4.0 License Status: Posted Version 3 posted You are reading this latest preprint version Show more versions Abstract High-temperature superconductors hold transformative potential across quantum computing, power transmission, and medical imaging technologies. This research employs multivariate polynomial regression, random search, and Python-based data analysis to predict potential chemical formulas for superconductors. By systematically analyzing thousands of characterized materials and interpolating critical temperature maxima across multidimensional graphs, we identified 29 viable candidates for high-temperature superconductors and 4 families of materials that could yield high temperature superconductivity in general. Our computational approach based on extensive data in 5 parameters provides a framework for screening potential high-temperature superconductor compounds with significant technological implications. This research paper declares high temperature superconductor candidates by using supervised machine learning to optimize parameters of the superconductor. It matches the parameters at maxima of the optimization polynomial and searches for the highest T c in the neighborhood of tested superconductors. We used random search to execute this under the restriction of a long-sustainable pressure value (1-10 atm). Materials Engineering Electronic Materials and Devices Materials Theory and Modeling material material science superconductors semiconductors electricity magnetism machine learning Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 3 posted You are reading this latest preprint version Show more versions 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-6637156","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533577863,"identity":"e3f0eb5f-b0fd-4a14-a70c-d0218f99920f","order_by":0,"name":"Ibrahim Nadeem","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYBACNgkGBmYGAxAzAYgNJHjsjzeAGBbEaimwkGE4cwCsF7c1YC0MMC0fKmwYbiRAxXEAPuneh58LCrbl8bcnH3x0A+gwxpnPr274USDBwN/enYDVYTLHjaVnGNwuljjzLNk4B6iFWTqn7GYP0GESZ85uwO6XNAZpHoPbiQ03csykQVrYpHPSbvAAtRhI5OLSwvwbpGX+jfzvv0FaeCTPpN38g18LG9iWDTdy2JhBWiQk2I/dxmuLzDE2a5CWjWeeGYMdZsCTw3ZbBqQXh1/kZ7cx3+b5cztx3vHkh59z/tTZG7Aff3bzzR8bOf72XqxasAEecMzyEKscBNgfkKJ6FIyCUTAKhj8AAJjoXVo/2NFjAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-3372-0185","institution":"Mountain House High School","correspondingAuthor":true,"prefix":"","firstName":"Ibrahim","middleName":"","lastName":"Nadeem","suffix":""}],"badges":[],"createdAt":"2025-05-11 01:45:41","currentVersionCode":3,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6637156/v3","doiUrl":"https://doi.org/10.21203/rs.3.rs-6637156/v3","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95312744,"identity":"02228a19-0ad6-414c-b834-945fb97122a8","added_by":"auto","created_at":"2025-11-06 15:50:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":589547,"visible":true,"origin":"","legend":"","description":"","filename":"SuperconductorResearchPaperfixed.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6637156/v3_covered_819e14a9-afb3-4785-8252-45d518554944.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Leveraging Machine Learning to Predict High-Temperature Superconductors","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Cambridge Centre of International Research","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":"material, material science, superconductors, semiconductors, electricity, magnetism, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-6637156/v3","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6637156/v3","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-temperature superconductors hold transformative potential across quantum computing, power transmission, and medical imaging technologies. This research employs multivariate polynomial regression, random search, and Python-based data analysis to predict potential chemical formulas for superconductors. By systematically analyzing thousands of characterized materials and interpolating critical temperature maxima across multidimensional graphs, we identified 29 viable candidates for high-temperature superconductors and 4 families of materials that could yield high temperature superconductivity in general. Our computational approach based on extensive data in 5 parameters provides a framework for screening potential high-temperature superconductor compounds with significant technological implications. This research paper declares high temperature superconductor candidates by using supervised machine learning to optimize parameters of the superconductor. It matches the parameters at maxima of the optimization polynomial and searches for the highest T\u003csub\u003ec\u003c/sub\u003e in the neighborhood of tested superconductors. We used random search to execute this under the restriction of a long-sustainable pressure value (1-10 atm).\u003c/p\u003e","manuscriptTitle":"Leveraging Machine Learning to Predict High-Temperature Superconductors","msid":"","msnumber":"","nonDraftVersions":[{"code":3,"date":"2025-11-05 19:44:00","doi":"10.21203/rs.3.rs-6637156/v3","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}},{"code":2,"date":"2025-10-22 19:46:23","doi":"10.21203/rs.3.rs-6637156/v2","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}},{"code":1,"date":"2025-05-13 04:29:52","doi":"10.21203/rs.3.rs-6637156/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":"d74bdc05-c199-423f-bb07-9088e21a4874","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56731817,"name":"Materials Engineering"},{"id":56731818,"name":"Electronic Materials and Devices"},{"id":56731819,"name":"Materials Theory and Modeling"}],"tags":[],"updatedAt":"2025-05-13T04:29:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 19:44:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v3","identity":"rs-6637156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6637156","identity":"rs-6637156","version":["v3"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}