Physics-informed neural operators for generalizable and label-free inference of temperature-dependent thermoelectric properties
Abstract Accurate characterization of temperature-dependent thermoelectric properties (TEPs), such as thermal conductivity and the Seebeck coefficient, is essential for modeling and design of thermoelectric devices. However, nonlinear temperature dependence and coupled transport behavior make forwar...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01769-1 |
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