Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy

Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy | 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 Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy Siya Zhu, ‪Doğuhan Sarıtürk, Raymundo Arróyave This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7172243/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Nov, 2025 Read the published version in npj Computational Materials → Version 1 posted 9 You are reading this latest preprint version Abstract High-entropy alloys (HEAs) have attracted increasing attention due to their unique structural and functional properties. In the study of HEAs, thermodynamic properties and phase stability play a crucial role, making phase diagram calculations significantly important. However, phase diagram calculations with conventional CALPHAD assessments based on experimental or ab-initio data can be expensive. With the emergence of machine-learning interatomic potentials (MLIPs), we have developed a program named PhaseForge, which integrates MLIPs into the Alloy Theoretic Automated Toolkit (ATAT) framework using our MLIP calculation library, MaterialsFramework, to enable efficient exploration of alloy phase diagrams. Moreover, our workflow can also serve as a benchmarking tool for evaluating the quality of different MLIPs. Physical sciences/Materials science Physical sciences/Physics Full Text Additional Declarations No competing interests reported. Supplementary Files SI.pdf Cite Share Download PDF Status: Published Journal Publication published 19 Nov, 2025 Read the published version in npj Computational Materials → Version 1 posted Editorial decision: Revision requested 25 Aug, 2025 Reviews received at journal 19 Aug, 2025 Reviews received at journal 17 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers agreed at journal 04 Aug, 2025 Reviewers invited by journal 28 Jul, 2025 Editor assigned by journal 26 Jul, 2025 Submission checks completed at journal 22 Jul, 2025 First submitted to journal 20 Jul, 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-7172243","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":492276783,"identity":"3fd4a05a-9c1f-42f5-ae86-1ee79fdd3af4","order_by":0,"name":"Siya Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDACZiBmbGCQQ+ISqcWYh3gtDBAtiT1Ea5Fv5zH8+HPH4fT97KfTJBgqrBMbCGkxOMxjLCF55nBuD0/uNgmGM+lEaGHmMZAwbANqYQBqYWw7TFiLfDOP8Y/EtsPpPPxvgVr+EaGF4TCPmcTBtsMJPBIgWxqI0GJwmK3MsrEt3bDnxtvNFgnH0o0JO6z/8OabP9us5dn7czfe+FBjLUvYYQwcBgh2AmHlIMD+gDh1o2AUjIJRMHIBANgePCtMpB0WAAAAAElFTkSuQmCC","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":true,"prefix":"","firstName":"Siya","middleName":"","lastName":"Zhu","suffix":""},{"id":492276784,"identity":"74c5d569-c4dd-473b-9a45-993215061e17","order_by":1,"name":"‪Doğuhan Sarıtürk","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"‪Doğuhan","middleName":"","lastName":"Sarıtürk","suffix":""},{"id":492276785,"identity":"211f4331-d849-4f05-86df-74001844e607","order_by":2,"name":"Raymundo Arróyave","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Raymundo","middleName":"","lastName":"Arróyave","suffix":""}],"badges":[],"createdAt":"2025-07-21 01:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7172243/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7172243/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41524-025-01814-z","type":"published","date":"2025-11-19T15:57:14+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96651410,"identity":"2fb03ad1-dcf4-4e6c-8ebe-ebdcb56624e6","added_by":"auto","created_at":"2025-11-24 16:14:33","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14820869,"visible":true,"origin":"","legend":"","description":"","filename":"Phasediagramworkflow.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7172243/v1_covered_94f68fbb-ec6c-415b-b4b0-fd8cf1479acf.pdf"},{"id":87951989,"identity":"20cd9a11-5638-44ef-9091-c3ea505bbbe3","added_by":"auto","created_at":"2025-07-30 17:47:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":706420,"visible":true,"origin":"","legend":"","description":"","filename":"SI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7172243/v1/f64a98b22d060ced692c2487.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning Potentials for Alloys: A Detailed Workflow to Predict Phase Diagrams and Benchmark Accuracy","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-7172243/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7172243/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-entropy alloys (HEAs) have attracted increasing attention due to their unique structural and functional properties. 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