  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.
