Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution

TL;DR

This paper introduces a machine-learning interatomic potential approach to significantly speed up the calculation of thermal conductivity in materials, matching the accuracy of traditional DFT-based methods but with reduced computational cost.

Contribution

The study presents a novel MLIP-based method that accelerates anharmonic force constant calculations, enabling faster thermal conductivity estimation without sacrificing accuracy.

Findings

MLIP method closely matches DFT results for thermal conductivity.

Significantly reduces computational time for anharmonic force calculations.

Applicable to bulk and 2D materials for thermal analysis.

Abstract

Accurate evaluation of the thermal conductivity of a material can be a challenging task from both experimental and theoretical points of view. In particular for the nanostructured materials, the experimental measurement of thermal conductivity is associated with diverse sources of uncertainty. As a viable alternative to experiment, the combination of density functional theory (DFT) simulations and the solution of Boltzmann transport equation is currently considered as the most trusted approach to examine thermal conductivity. The main bottleneck of the aforementioned method is to acquire the anharmonic interatomic force constants using the computationally demanding DFT calculations. In this work we propose a substantially accelerated approach for the evaluation of anharmonic interatomic force constants via employing machine-learning interatomic potentials (MLIPs) trained over short ab-initio molecular dynamics trajectories. The remarkable accuracy of the proposed accelerated method is confirmed by comparing the estimated thermal conductivities of several bulk and two-dimensional materials with those computed by the full-DFT approach. The MLIP-based method proposed in this study can be employed as a standard tool, which would substantially accelerate and facilitate the estimation of lattice thermal conductivity in comparison with the commonly used full-DFT solution.