Physics-Informed Neural Operators for Generalizable and Label-Free Inference of Temperature-Dependent Thermoelectric Properties

Physics-Informed Neural Operators for Generalizable and Label-Free Inference of Temperature-Dependent Thermoelectric Properties | 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 Physics-Informed Neural Operators for Generalizable and Label-Free Inference of Temperature-Dependent Thermoelectric Properties Hyeonbin Moon¹, Songho Lee¹, Wabi Demeke¹, Byungki Ryu², Seunghwa Ryu¹ This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6850934/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Aug, 2025 Read the published version in npj Computational Materials → Version 1 posted 10 You are reading this latest preprint version Abstract Accurate characterization of temperature-dependent thermoelectric properties (TEPs), such as thermal conductivity and the Seebeck coefficient, is essential for reliable modeling and efficient design of thermoelectric devices. However, their nonlinear temperature dependence and coupled transport behavior make both forward simulation and inverse identification difficult, particularly under sparse measurement conditions. In this study, we develop a physics-informed machine learning approach that employs physics-informed neural networks (PINN) for solving forward and inverse problems in thermoelectric systems, and neural operators (PINO) to enable generalization across diverse material systems. The PINN enables field reconstruction and material property inference by embedding governing transport equations into the loss function, while the PINO generalizes this inference capability across diverse materials without retraining. Trained on simulated data for 20 p-type materials and evaluated on 60 unseen materials, the PINO model demonstrates accurate and label-free inference of TEPs using only sparse field data. The proposed framework offers a scalable, generalizable, and data-efficient approach for thermoelectric property identification, paving the way for high-throughput screening and inverse design of advanced thermoelectric materials. Physical sciences/Materials science/Materials for energy and catalysis/Thermoelectrics Physical sciences/Physics/Applied physics Physical sciences/Engineering Thermoelectric properties (TEPs) PDEs forward/inverse problem PINNs PINOs Full Text Additional Declarations No competing interests reported. Supplementary Files ManuscriptTEGPINOsupplementary.pdf Cite Share Download PDF Status: Published Journal Publication published 22 Aug, 2025 Read the published version in npj Computational Materials → Version 1 posted Editorial decision: Revision requested 22 Jul, 2025 Reviews received at journal 04 Jul, 2025 Reviews received at journal 29 Jun, 2025 Reviewers agreed at journal 21 Jun, 2025 Reviewers agreed at journal 20 Jun, 2025 Reviewers agreed at journal 20 Jun, 2025 Reviewers invited by journal 19 Jun, 2025 Editor assigned by journal 13 Jun, 2025 Submission checks completed at journal 11 Jun, 2025 First submitted to journal 09 Jun, 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-6850934","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":474523217,"identity":"85355b4e-84e1-4bac-b0c5-87e45efd0e2b","order_by":0,"name":"Hyeonbin Moon¹","email":"","orcid":"","institution":"Korea Advanced Institute of Science and Technology (KAIST)","correspondingAuthor":false,"prefix":"","firstName":"Hyeonbin","middleName":"","lastName":"Moon¹","suffix":""},{"id":474523218,"identity":"ebcdb3b5-7413-44c9-b831-07e5bd86d942","order_by":1,"name":"Songho Lee¹","email":"","orcid":"","institution":"Korea Advanced Institute of Science and 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