Machine Learning-Assisted Prediction of Phonon Transport and Thermoelectric Properties in CuGaTe2
Abstract
Thermoelectric materials, which enable the direct conversion of waste heat into electricity, offer a sustainable pathway to address energy scarcity and environmental concerns. The efficiency of thermoelectric conversion is characterized by the dimensionless figure of merit, where optimizing electrical transport while suppressing lattice thermal conductivity is crucial. In this study, we investigate the structural, bonding, phonon, and thermoelectric properties of CuGaTe2 using first-principles calculations combined with self-consistent phonon theory and four-phonon scattering analysis. Our findings reveal distinct layered bonding features, consisting of ionic Cu–Te bonds and polar covalent Ga–Te bonds, which create a pronounced bonding hierarchy and induce strong local anharmonicity. Based on Heyd–Scuseria–Ernzerhof hybrid functional band structures and ab initio scattering and transport electronic transport calculations, p-type CuGaTe2 achieves a maximum power factor of 6.65 mWm–1 K–2 and a peak ZT of approximately 1.76 at 900 K, surpassing its n-type counterpart. These results establish CuGaTe2 as a promising p-type thermoelectric material for intermediate- to high-temperature applications and highlight the critical role of four-phonon scattering in accurately predicting thermal transport in strongly anharmonic materials.