Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations

Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles 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 Article Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations Masato Ohnishi, Tianqi Deng, Pol Torres, Zhihao Xu, Terumasa Tadano, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6552075/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Apr, 2026 Read the published version in npj Computational Materials → Version 1 posted 11 You are reading this latest preprint version Abstract Understanding the anharmonic phonon properties of crystal compounds—such as phonon lifetimes and thermal conductivities—is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6,000 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enable the identification of materials exhibiting extreme thermal conductivities—both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials. Physical sciences/Materials science/Theory and computation/Atomistic models Physical sciences/Materials science/Theory and computation/Scaling laws Physical sciences/Materials science Physical sciences/Materials science/Theory and computation Physical sciences/Materials science/Theory and computation/Computational methods Full Text Additional Declarations No competing interests reported. Supplementary Files OhnishiAnharmonicPhononDatabaseSI.pdf Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2026 Read the published version in npj Computational Materials → Version 1 posted Editorial decision: Revision requested 13 Jun, 2025 Reviews received at journal 04 Jun, 2025 Reviews received at journal 28 May, 2025 Reviews received at journal 19 May, 2025 Reviewers agreed at journal 14 May, 2025 Reviewers agreed at journal 09 May, 2025 Reviewers agreed at journal 09 May, 2025 Reviewers invited by journal 09 May, 2025 Editor assigned by journal 30 Apr, 2025 Submission checks completed at journal 29 Apr, 2025 First submitted to journal 28 Apr, 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. 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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-6552075","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":454397438,"identity":"852e8508-9ffc-462d-8806-49b006d2521e","order_by":0,"name":"Masato Ohnishi","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Masato","middleName":"","lastName":"Ohnishi","suffix":""},{"id":454397439,"identity":"7030efec-5a19-4fe0-a422-4646b4f4154c","order_by":1,"name":"Tianqi Deng","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Tianqi","middleName":"","lastName":"Deng","suffix":""},{"id":454397440,"identity":"6e9471a2-9ba7-4cd7-b2d5-9c75de232ea7","order_by":2,"name":"Pol Torres","email":"","orcid":"","institution":"Eurecat Technology Centre of Catalonia","correspondingAuthor":false,"prefix":"","firstName":"Pol","middleName":"","lastName":"Torres","suffix":""},{"id":454397441,"identity":"038f02e0-99d5-48dd-b1ad-98fe62bf295c","order_by":3,"name":"Zhihao Xu","email":"","orcid":"","institution":"University of Notre Dame","correspondingAuthor":false,"prefix":"","firstName":"Zhihao","middleName":"","lastName":"Xu","suffix":""},{"id":454397442,"identity":"ae3770cd-4594-46bc-844c-3e96de19ef91","order_by":4,"name":"Terumasa Tadano","email":"","orcid":"","institution":"National Institute for Materials Science","correspondingAuthor":false,"prefix":"","firstName":"Terumasa","middleName":"","lastName":"Tadano","suffix":""},{"id":454397443,"identity":"8c775fb7-af5c-424e-abb9-f80c9aab7ca1","order_by":5,"name":"Haoming Zhang","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Haoming","middleName":"","lastName":"Zhang","suffix":""},{"id":454397444,"identity":"84720d16-e44b-4f2c-b972-263d386f124a","order_by":6,"name":"Wei Nong","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Nong","suffix":""},{"id":454397445,"identity":"7cf77280-c208-40b7-b839-56beb66fbf90","order_by":7,"name":"Masatoshi Hanai","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Masatoshi","middleName":"","lastName":"Hanai","suffix":""},{"id":454397446,"identity":"f79283bc-88db-485b-a9c2-7531709348dd","order_by":8,"name":"Zhiting Tian","email":"","orcid":"","institution":"Cornell University","correspondingAuthor":false,"prefix":"","firstName":"Zhiting","middleName":"","lastName":"Tian","suffix":""},{"id":454397447,"identity":"0b380010-177f-4f04-af8f-2cdb94208701","order_by":9,"name":"Ming Hu","email":"","orcid":"","institution":"University of South Carolina","correspondingAuthor":false,"prefix":"","firstName":"Ming","middleName":"","lastName":"Hu","suffix":""},{"id":454397448,"identity":"e0da32fa-6395-461d-a772-cba5f531cd0f","order_by":10,"name":"Xiulin Ruan","email":"","orcid":"","institution":"Purdue University","correspondingAuthor":false,"prefix":"","firstName":"Xiulin","middleName":"","lastName":"Ruan","suffix":""},{"id":454397449,"identity":"9f0bfb7d-9575-4bcd-ae16-0f27271d4067","order_by":11,"name":"Ryo Yoshida","email":"","orcid":"","institution":"The Graduate University for Advanced Studies, SOKENDAI","correspondingAuthor":false,"prefix":"","firstName":"Ryo","middleName":"","lastName":"Yoshida","suffix":""},{"id":454397450,"identity":"dc4023b4-07f1-4cc6-a867-a4ed9c7bd6e7","order_by":12,"name":"Toyotaro Suzumura","email":"","orcid":"","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Toyotaro","middleName":"","lastName":"Suzumura","suffix":""},{"id":454397451,"identity":"73515310-5f27-4233-a497-5b05ebc7b3f7","order_by":13,"name":"Lucas Lindsay","email":"","orcid":"","institution":"Oak Ridge National Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Lucas","middleName":"","lastName":"Lindsay","suffix":""},{"id":454397452,"identity":"a9ab429d-7c1d-4970-8422-560cf500bbb3","order_by":14,"name":"Alan J. 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