A physics-informed neural operator trained on 20 simulated thermoelectric materials infers thermal conductivity and Seebeck coefficient for 60 unseen materials from six sparse measurements, with test R-squared above 0.97.
A High-Quality Thermoelectric Material Database with Self-Consistent ZT Filtering
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abstract
This study presents a curated thermoelectric material database, teMatDb, constructed by digitizing literature-reported data. It includes temperature-dependent thermoelectric properties (TEPs), Seebeck coefficient, electrical resistivity, thermal conductivity, and figure of merit (ZT), along with metadata on materials and their corresponding publications. A self-consistent ZT (Sc-ZT) filter set was developed to measure ZT errors by comparing reported ZT's from figures with ZT's recalculated from digitized TEPs. Using this Sc-ZT protocol, we generated tMatDb272, comprising 14,717 temperature-property pairs from 272 high-quality TEP sets across 262 publications. The method identifies various types of ZT errors, such as resolution error, publication bias, ZT overestimation, interpolation and extrapolation error, and digitization noise, and excludes inconsistent samples from the dataset. teMatDb272 and the Sc-ZT filtering framework offer a robust dataset for data-driven and machine-learning-based materials design, device modeling, and future thermoelectric research.
fields
cond-mat.mtrl-sci 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Physics-Informed Neural Operators for Generalizable and Label-Free Inference of Temperature-Dependent Thermoelectric Properties
A physics-informed neural operator trained on 20 simulated thermoelectric materials infers thermal conductivity and Seebeck coefficient for 60 unseen materials from six sparse measurements, with test R-squared above 0.97.