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The spectral conductivity, i.e., the electrical conductivity as a function ofthe Fermi energy, is a cornerstone in determining the thermoelectric transportproperties of electrons. However, the spectral conductivity depends onsample-specific properties such as carrier concentrations, vacancies, chargeimpurities, chemical compositions, and material microstructures, making itdifficult to relate the experimental result with the theoretical predictiondirectly. Here, we propose a data-driven approach based on machine learning toreconstruct the spectral conductivity and chemical potential from thethermoelectric transport data. Using this machine learning method, we firstdemonstrate that the spectral conductivity and temperature-dependent chemicalpotentials can be recovered within a simple toy model. In a second step, weapply our method to experimental data in doped one-dimensional tellurideTa$_4$SiTe$_4$~[T. Inohara, \textit{et al.}, Appl. Phys. Lett. \textbf{110},183901 (2017)] to reconstruct the spectral conductivity and chemical potentialfor each sample. Furthermore, the thermal conductivity of electrons and themaximal figure of merit $ZT$ are estimated from the reconstructed spectralconductivity, which provides accurate estimates beyond the Wiedemann-Franz law.Our study clarifies the connection between the thermoelectric transportproperties and the low-energy electronic states of real materials, andestablishes a promising route to incorporate experimental data into traditionaltheory-driven workflows.

Data-driven reconstruction of spectral conductivity and chemical potential from thermoelectric transport data