High-throughput discovery of metal oxides with high thermoelectric performance via interpretable feature engineering on small data

TL;DR

This paper presents a data-driven, interpretable machine learning framework combined with high-throughput calculations to identify metal oxides with high thermoelectric performance at elevated temperatures, focusing on small data challenges.

Contribution

It introduces a novel interpretable feature engineering approach using symbolic regression and high-throughput transport calculations to discover promising thermoelectric metal oxides from large databases.

Findings

Identified 28 metal oxides with high power factors above 50 μW cm^{-1} K^{-2}.

Metal oxides with elements like Ce, Sn, and Pb tend to have high power factors at high temperatures.

The framework effectively screens large compound databases for thermoelectric applications.

Abstract

In this work, we have proposed a data-driven screening framework combining the interpretable machine learning with high-throughput calculations to identify a series of metal oxides that exhibit both high-temperature tolerance and high power factors. Aiming at the problem of weak generalization ability of small data with power factors at high temperatures, we employ symbolic regression for feature creation which enhances the robustness of the model while preserving the physical meaning of features. 33 candidate metal oxides are finally targeted for high-temperature thermoelectric applications from a pool of 48,694 compounds in the Materials Project database. The Boltzmann transport theory is utilized to perform electrical transport properties calculations at 1,000 K. The relaxation time is approximated by employing constant electron-phonon coupling based on the deformation potential theory. Considering band degeneracy, the electron group velocity is obtained using the momentum matrix element method, yielding 28 materials with power factors greater than 50 ${\mu}W cm^{-1} K^{-2} $. The high-throughput framework we proposed is instrumental in the selection of metal oxides for high-temperature thermoelectric applications. Furthermore, our data-driven analysis and transport calculation suggest that metal oxides rich in elements such as cerium (Ce), tin (Sn), and lead (Pb) tend to exhibit high power factors at high temperatures.