Methods for determining the Hubbard U parameter in DFT+U calculations for new energy material

Methods for determining the Hubbard U parameter in DFT+U calculations for new energy material | 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 Research Article Methods for determining the Hubbard U parameter in DFT+U calculations for new energy material Yujie Jiang, Shengbing Dong, Tao Yang, Yuqi Sun, Shuang Liu, Linlin Zhou, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9485062/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Accurate simulation of strongly correlated electron systems is a major challenge for designing clean and efficient energy materials. Density functional theory (DFT) is widely used for electronic structure calculations, but it fails for systems with localized d or f orbital electrons because it neglects dynamic electron-electron correlations. The DFT + U method, rooted in the Hubbard model, overcomes this limitation by balancing computational efficiency and accuracy. Its predictive power strongly depends on the correct choice of the Hubbard U parameter. This review first explains the theoretical basis of the Hubbard model and the DFT + U framework. It then systematically describes U determination techniques, including experimental fitting, linear response methods and their derivatives, pseudo hybrid functional approaches, and machine learning enabled optimization strategies. We compare their computational merits and applicability. Finally, we discuss emerging paradigms, such as combining first principles screening with adaptive machine learning workflows. These advances promise to improve the precision and scalability of strongly correlated system modeling and accelerate the rational design of next generation energy materials. Hubbard U DFT + U strongly correlated electron systems machine learning linear response methods Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 29 Apr, 2026 Reviewers invited by journal 29 Apr, 2026 Editor assigned by journal 22 Apr, 2026 First submitted to journal 21 Apr, 2026 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. 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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-9485062","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":631439321,"identity":"dc39c928-e07a-40e3-a760-17a7e5d2c784","order_by":0,"name":"Yujie Jiang","email":"","orcid":"","institution":"University of Science and Technology Beijing","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Jiang","suffix":""},{"id":631439322,"identity":"479c9035-29e9-4228-88bb-75d80d71a89c","order_by":1,"name":"Shengbing Dong","email":"","orcid":"","institution":"University of Science and Technology 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