Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark

Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark | 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 Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark Enda Xiao, Terumasa Tadano This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7514318/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Feb, 2026 Read the published version in npj Computational Materials → Version 1 posted 10 You are reading this latest preprint version Abstract We present a machine learning-accelerated high-throughput (HTP) workflow for the discovery of functional materials. As a test case, quaternary and all-d Heusler compounds were screened for stable compounds with large magnetocrystalline anisotropy energy (Eaniso). Structure optimization and evaluation of formation energy and energy above the convex hull were performed using the eSEN-30M-OAM interatomic potential, while local magnetic moments, phonon stability, magnetic stability, and Eaniso were predicted by eSEM models trained on our DxMag Heusler database. A frozen transfer learning strategy was employed to improve accuracy. Candidate compounds identified by the ML-HTP workflow were validated with density functional theory, confirming high predictive precision. We also benchmarked the performance of different MLIPs, and discussed the fidelity of local magnetic moment prediction and its extension to other magnetic materials. Physical sciences/Materials science Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Supplementary Files supplementalmaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 19 Feb, 2026 Read the published version in npj Computational Materials → Version 1 posted Editorial decision: Revision requested 09 Dec, 2025 Reviews received at journal 08 Dec, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers invited by journal 14 Sep, 2025 Editor assigned by journal 06 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 02 Sep, 2025 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. 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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-7514318","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":515525379,"identity":"079ea05f-4231-44c7-ba67-8f96885a9244","order_by":0,"name":"Enda Xiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACg/MHGx8w/mFgbGBvYDgAFUzAr+Uw82EDxgagFp4DRGqRbGZLkwBrkcCvEAH4mXnMgFruyPbPfHvw0I0/DHb9DAzPHuDTwgbW8uOZ8YzbeQmHc9sYkmc2MKQbENbScDix4XaOweHcBoZkgwMMaRL4Hcb/Daxl/s0zBodz/hChRbIZasuGGzxALWwMdgS1GBzmMTZIbDhsvPEMyGFtEgmSzQT8YnD+jOGDj38Oy847fsb4c84fG3t+9p60B/i0gEECgimR2MDMk0ZQBwqwZ2BgP0aallEwCkbBKBjuAAA3jVB4j6r8kQAAAABJRU5ErkJggg==","orcid":"","institution":"National Institute for Materials Science","correspondingAuthor":true,"prefix":"","firstName":"Enda","middleName":"","lastName":"Xiao","suffix":""},{"id":515525380,"identity":"36ac6d76-b8ef-4233-9b7d-c9d0365bd0e7","order_by":1,"name":"Terumasa Tadano","email":"","orcid":"","institution":"National Institute for Materials Science","correspondingAuthor":false,"prefix":"","firstName":"Terumasa","middleName":"","lastName":"Tadano","suffix":""}],"badges":[],"createdAt":"2025-09-02 07:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7514318/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7514318/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41524-026-02013-0","type":"published","date":"2026-02-19T15:56:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91901901,"identity":"032c3276-481d-418c-94ab-893555650802","added_by":"auto","created_at":"2025-09-22 21:55:07","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3942,"visible":true,"origin":"","legend":"","description":"","filename":"4043c1d2720d42ae9e2abc9481821cfb.json","url":"https://assets-eu.researchsquare.com/files/rs-7514318/v1/0e5c5eee1acf40e797875cf4.json"},{"id":103251025,"identity":"784ed5a8-574d-4bca-9f1c-5c4e25224198","added_by":"auto","created_at":"2026-02-23 16:01:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1625535,"visible":true,"origin":"","legend":"","description":"","filename":"npjsub.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7514318/v1_covered_03654891-d334-4ebc-b02d-dc197220dab7.pdf"},{"id":91901902,"identity":"e3bb07a8-66ff-420f-bb48-72040ad099db","added_by":"auto","created_at":"2025-09-22 21:55:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1500873,"visible":true,"origin":"","legend":"","description":"","filename":"supplementalmaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7514318/v1/3a3f1bd69f62f0f67c743962.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Accurate Screening of Functional Materials with Machine-Learning Potential and Transfer-Learned Regressions: Heusler Alloy Benchmark","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"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":"","lastPublishedDoi":"10.21203/rs.3.rs-7514318/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7514318/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"We present a machine learning-accelerated high-throughput (HTP) workflow for the discovery of functional materials. 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