From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows

From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows | 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 From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows Sarath Menon, Yury Lysogorskiy, Alexander Knoll, Niklas Leimeroth, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4067750/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Nov, 2024 Read the published version in npj Computational Materials → Version 1 posted 9 You are reading this latest preprint version Abstract We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry. Physical sciences/Materials science/Condensed-matter physics/Phase transitions and critical phenomena Physical sciences/Materials science/Condensed-matter physics/Phase transitions and critical phenomena Physical sciences/Materials science/Condensed-matter physics/Phase transitions and critical phenomena Physical sciences/Physics/Condensed-matter physics/Structure of solids and liquids Physical sciences/Physics/Condensed-matter physics/Structure of solids and liquids Full Text Additional Declarations There is a conflict of interest Jörg Neugebauer is an associated editor for npj Computational Materials Supplementary Files supplementarymaterial.pdf Cite Share Download PDF Status: Published Journal Publication published 17 Nov, 2024 Read the published version in npj Computational Materials → Version 1 posted Editorial decision: accept 16 Oct, 2024 Review # 1 received at journal 07 Aug, 2024 Reviewer # 2 agreed at journal 25 Jul, 2024 Reviewers invited by journal 25 Jul, 2024 Reviewer # 1 agreed at journal 25 Jul, 2024 Submission checks completed at journal 18 Jul, 2024 First submitted to journal 17 Jul, 2024 Unknown event 04 Jul, 2024 Editor assigned by journal 28 Jun, 2024 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. 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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-4067750","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":337076534,"identity":"bc2fc712-1ed6-4d05-83cc-d9af4be6c1f9","order_by":0,"name":"Sarath 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