High-Throughput Prediction of Finite-Temperature Properties using the Quasi-Harmonic Approximation

TL;DR

This paper presents an automated high-throughput method combining the Quasi-Harmonic Approximation with the Automatic Phonon Library within the AFLOW framework to predict finite-temperature properties of materials accurately.

Contribution

It introduces an automated, robust, and accurate approach for calculating thermal properties of materials using QHA-APL within AFLOW, enabling large-scale data generation.

Findings

QHA-APL predicts thermal properties with less than 28% deviation from experiments.

The method is reliable across various classes of solids.

It facilitates large data set generation for materials discovery.

Abstract

In order to calculate thermal properties in automatic fashion, the Quasi-Harmonic Approximation (QHA) has been combined with the Automatic Phonon Library (APL) and implemented within the AFLOW framework for high-throughput computational materials science. As a benchmark test to address the accuracy of the method and implementation, the specific heats, thermal expansion coefficients, Gr\"uneisen parameters and bulk moduli have been calculated for 130 compounds. It is found that QHA-APL can reliably predict such values for several different classes of solids with root mean square relative deviation smaller than 28% with respect to experimental values. The automation, robustness, accuracy and precision of QHA-APL enable the computation of large material data sets, the implementation of repositories containing thermal properties, and finally can serve the community for data mining and machine learning studies.