Predicting the lattice thermal conductivity of solids by solving the Boltzmann transport equation: AFLOW - AAPL an automated, accurate and effcient framework

TL;DR

This paper introduces AAPL, an automated framework integrated with AFLOW, that efficiently calculates lattice thermal conductivity of solids by solving the Boltzmann transport equation, reducing computational costs and increasing automation for materials discovery.

Contribution

The paper presents AAPL, a novel automated tool that improves the efficiency and accuracy of lattice thermal conductivity calculations within high-throughput materials frameworks.

Findings

AAPL effectively computes interatomic force constants using symmetry analysis.

It accurately predicts thermal conductivity with reduced computational effort.

Combining AAPL with ACBN0 enhances accuracy for correlated materials.

Abstract

One of the most accurate approaches for calculating lattice thermal conductivity, $\kappa_l$, is solving the Boltzmann transport equation starting from third-order anharmonic force constants. In addition to the underlying approximations of ab-initio parameterization, two main challenges are associated with this path. High computational costs and lack of automation in the frameworks using this methodology affect the discovery rate of novel materials with ad-hoc properties. Here, we present the Automatic-Anharmonic-Phonon-Library, AAPL. It efficiently computes interatomic force constants by making effective use of crystal symmetry analysis, it solves the Boltzmann transport equation to obtain $\kappa_l$, and allows a fully integrated operation with minimum user intervention, a rational addition to the current high-throughput accelerated materials development framework AFLOW. We show an "experiment versus theory" study of the approach, we compare accuracy and speed with respect to other available packages, and for materials characterized by strong electron localization and correlation, we demonstrate that it is possible to improve accuracy without increasing computational requirements by combining AAPL with the pseudo-hybrid functional ACBN0.