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

Abstract

The spectral conductivity, i.e., the electrical conductivity as a function of the Fermi energy, is a cornerstone in determining the thermoelectric transport properties of electrons. However, the spectral conductivity depends on sample-specific properties such as carrier concentrations, vacancies, charge impurities, chemical compositions, and material microstructures, making it difficult to relate the experimental result with the theoretical prediction directly. Here, we propose a data-driven approach based on machine learning to reconstruct the spectral conductivity and chemical potential from the thermoelectric transport data. Using this machine learning method, we first demonstrate that the spectral conductivity and temperature-dependent chemical potentials can be recovered within a simple toy model. In a second step, we apply our method to experimental data in doped one-dimensional telluride Ta${}_4$SiTe${}_4$~[T. Inohara, \textit{et al.}, Appl. Phys. Lett. \textbf{110}, 183901 (2017)] to reconstruct the spectral conductivity and chemical potential for each sample. Furthermore, the thermal conductivity of electrons and the maximal figure of merit $ZT$ are estimated from the reconstructed spectral conductivity, which provides accurate estimates beyond the Wiedemann-Franz law. Our study clarifies the connection between the thermoelectric transport properties and the low-energy electronic states of real materials, and establishes a promising route to incorporate experimental data into traditional theory-driven workflows.

arXiv comment10 pages, 9 figures. Accepted version for Journal of the Physical Society of Japan

References in corpus (19)

• BoltzTraP. A code for calculating band-structure dependent quantities
• BoltzTraP2, a program for interpolating band structures and calculating semi-classical transport coefficients
• AFLOW: An automatic framework for high-throughput materials discovery
• Discovery of low thermal conductivity compounds with first-principles anharmonic lattice dynamics calculations and Bayesian optimization
• Finding unprecedentedly low-thermal-conductivity half-Heusler semiconductors via high-throughput materials modeling
• Thermoelectric Response of an Interacting Two-Dimensional Electron Gas in Quantizing Magnetic Field
• A Data Ecosystem to Support Machine Learning in Materials Science
• Designing nanostructures for interfacial phonon transport via Bayesian optimization
• Electronic Structure Calculation by First Principles for Strongly Correlated Electron Systems
• Materials discovery and properties prediction in thermal transport via materials informatics: a mini-review
• Sparse modeling approach to analytical continuation of imaginary-time quantum Monte Carlo data
• High-throughput exploration of alloying as design strategy for thermoelectrics
• General Formula for the Thermoelectric Transport Phenomena based on the Fermi Liquid Theory: Thermopower, Nernst Coefficient, and Thermal Conductivity
• Sparse Modeling in Quantum Many-Body Problems
• Large Thermoelectric Power Factor at Low Temperatures in One-Dimensional Telluride Ta4SiTe4
• Analytical continuation of imaginary axis data using maximum entropy
• Two Pressure-induced Superconducting Transitions in SnBi${}_2$Se${}_4$ Explored by Data-driven Materials Search: New Approach to Develop Novel Functional Materials Including Thermoelectric and Superconducting Materials
• Effect of paramagnon drag on thermoelectric transport properties: Linear response theory
• Theory of Thermal Conductivity on Excitonic Insulator