Discovery of low thermal conductivity compounds with first-principles anharmonic lattice dynamics calculations and Bayesian optimization

TL;DR

This study combines first-principles anharmonic lattice dynamics calculations with Bayesian optimization to efficiently identify new compounds with exceptionally low thermal conductivity, advancing thermoelectric material discovery.

Contribution

It introduces a novel virtual screening approach that integrates first-principles calculations with Bayesian optimization to discover low thermal conductivity materials.

Findings

Discovered 221 low LTC compounds from a library of 54,779.

Identified two compounds with low LTC and band gap < 1 eV as promising thermoelectric candidates.

Demonstrated the effectiveness of the combined computational strategy for materials discovery.

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.