Magnetic Structures Database from Symmetry-aided High-Throughput Calculations

Magnetic Structures Database from Symmetry-aided High-Throughput Calculations | 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 Physical Sciences - Article Magnetic Structures Database from Symmetry-aided High-Throughput Calculations Xiangang Wan, Hanjing Zhou, Yuxuan Mu, Dingwen Zhang, Hangbing Chu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8672504/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 Magnetic structures, which play a central role in determining their physical properties, are known for only very limited compounds. Traditional theoretical approaches to predicting magnetic structures predominantly rely on first-principles calculations. A key challenge of these methods is their requirement for initial magnetic configurations as inputs, which theoretically possess infinite possibilities. In this work, we introduce a strategy based on irreducible representation basis vectors that effectively narrows down the vast space of potential magnetic configurations to a finite set, typically comprising around 20 candidates per material. Despite this significant reduction, the compact input sets generated by our method already encompass the experimental magnetic structures for 253 out of 302 benchmark materials (83.8%) from the MAGNDATA database. These materials have propagation vectors q = 0 and unit cells containing up to 40 atoms, all within the Landau framework. Subsequent first-principles calculations correctly identify the magnetic structure in 198 of these cases. We further apply our highly efficient method to 8,422 stoichiometric transition-metal compounds with fewer than 30 atoms per unit cell in the Inorganic Crystal Structure Database, and establish a magnetic structure database containing 2,906 magnetic materials. To demonstrate its utility, we use this database for the systematic exploration of magnetic topological phases and altermagnets, identifying 1,070 and 392 materials, respectively. Physical sciences/Physics/Condensed-matter physics/Magnetic properties and materials Physical sciences/Physics/Condensed-matter physics/Topological matter/Topological insulators Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SMNature1.23.pdf Supplemental Materials for “Magnetic Structures Database from Symmetry-aided High-Throughput Calculations” SupplementalMaterials.pdf Supplemental Materials for “Magnetic Structures Database from Symmetry-aided High-Throughput Calculations” 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-8672504","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Physical Sciences - Article","associatedPublications":[],"authors":[{"id":583099966,"identity":"02c837be-f04f-4845-bbdd-78ad6f641554","order_by":0,"name":"Xiangang 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Traditional theoretical approaches to predicting magnetic structures predominantly rely on first-principles calculations. A key challenge of these methods is their requirement for initial magnetic configurations as inputs, which theoretically possess infinite possibilities. In this work, we introduce a strategy based on irreducible representation basis vectors that effectively narrows down the vast space of potential magnetic configurations to a finite set, typically comprising around 20 candidates per material. Despite this significant reduction, the compact input sets generated by our method already encompass the experimental magnetic structures for 253 out of 302 benchmark materials (83.8%) from the MAGNDATA database. These materials have propagation vectors q = 0 and unit cells containing up to 40 atoms, all within the Landau framework. Subsequent first-principles calculations correctly identify the magnetic structure in 198 of these cases. We further apply our highly efficient method to 8,422 stoichiometric transition-metal\r\ncompounds with fewer than 30 atoms per unit cell in the Inorganic Crystal Structure Database, and establish a magnetic structure database containing 2,906 magnetic materials. 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