High Throughput combinatorial method for fast and robust prediction of lattice thermal conductivity

TL;DR

This paper introduces a fast, combinatorial method to accurately predict lattice thermal conductivity using the Slack equation, optimizing definitions of key variables for improved material screening efficiency.

Contribution

It presents a novel combinatorial approach to identify optimal variable definitions in the Slack equation for better thermal conductivity predictions.

Findings

The method outperforms traditional Debye-based screening models.

It achieves comparable accuracy to more expensive ab-initio methods.

The approach is validated on 42 compounds, demonstrating robustness.

Abstract

The lack of computationally inexpensive and accurate ab-initio based methodologies to predict lattice thermal conductivity, without computing the anharmonic force constants or time-consuming ab-initio molecular dynamics, is one of the obstacles preventing the accelerated discovery of new high or low thermal conductivity materials. The Slack equation is the best alternative to other more expensive methodologies but is highly dependent on two variables: the acoustic Debye temperature, $\theta_a$, and the Gr\"{u}neisen parameter, $\gamma$. Furthermore, different definitions can be used for these two quantities depending on the model or approximation. In this article, we present a combinatorial approach to elucidate which definitions of both variables produce the best predictions of the lattice thermal conductivity, $\kappa_l$. A set of 42 compounds was used to test accuracy and robustness of all possible combinations. This approach is ideal for obtaining more accurate values than fast screening models based on the Debye model, while being significantly less expensive than methodologies that solve the Boltzmann transport equation.