Prediction of Low-Thermal-Conductivity Compounds with First-Principles Anharmonic Lattice-Dynamics Calculations and Bayesian Optimization

TLDR

Low lattice thermal conductivity is crucial for high‑efficiency thermoelectrics, yet existing strategies have only explored a limited chemical space. This study performs a virtual screening of 54,779 compounds to identify candidates with low LTC. Bayesian optimization guided by first‑principles anharmonic lattice‑dynamics LTC calculations on 101 initial compounds drives the search. The approach yields 221 materials with very low LTC, including two with band gaps below 1 eV that are promising thermoelectrics, and demonstrates the method’s broader applicability for chemistry‑optimized materials.

Abstract

Compounds of low lattice thermal conductivity (LTC) are essential for seeking thermoelectric materials with high conversion efficiency. Some strategies have been used to decrease LTC. However, such trials have yielded successes only within a limited exploration space. Here we report the virtual screening of a library containing 54,779 compounds. Our strategy is to search the library through Bayesian optimization using for the initial data the LTC obtained from first-principles anharmonic lattice dynamics calculations for a set of 101 compounds. We discovered 221 materials with very low LTC. Two of them have even an electronic band gap < 1 eV, what makes them exceptional candidates for thermoelectric applications. In addition to those newly discovered thermoelectric materials, the present strategy is believed to be powerful for many other applications in which chemistry of materials are required to be optimized.