High-Throughput Computational Screening of thermal conductivity, Debye temperature and Gr\"uneisen parameter using a quasi-harmonic Debye Model

TL;DR

This paper introduces a computationally efficient quasi-harmonic Debye model integrated into high-throughput frameworks to predict thermal properties like Debye temperature and thermal conductivity, aiding rapid materials screening.

Contribution

The study implements the AGL method within AFLOW and Materials Project frameworks, demonstrating its reliability in ranking thermal conductivities and highlighting Debye temperature as a useful screening descriptor.

Findings

AGL method reliably predicts thermal conductivity ranking.

Debye temperature often better correlates with experimental thermal conductivity.

AGL offers a cheaper alternative to ab initio approaches for thermal property prediction.

Abstract

The quasi-harmonic Debye approximation has been implemented within the AFLOW and Materials Project frameworks for high-throughput computational science (Automatic Gibbs Library, AGL), in order to calculate thermal properties such as the Debye temperature and the thermal conductivity of materials. We demonstrate that the AGL method, which is significantly cheaper computationally compared to the fully ab initio approach, can reliably predict the ordinal ranking of the thermal conductivity for several different classes of semiconductor materials. We also find that for the set of 182 materials investigated in this work the Debye temperature, calculated with the AGL, is often a better predictor of the ordinal ranking of the experimental thermal conductivities than the calculated thermal conductivity. The Debye temperature is thus a potential descriptor for high-throughput screening of the thermal properties of materials.