Revisiting thermal transport in CuCl: First-principles calculations and machine learning force fields

TL;DR

This study combines first-principles calculations and machine learning force fields to accurately predict and analyze the thermal conductivity of CuCl, highlighting the importance of anharmonic effects and four-phonon scattering.

Contribution

It introduces a comprehensive framework integrating IFC renormalization, four-phonon scattering, and machine learning to model thermal transport in strongly anharmonic materials.

Findings

Excellent agreement with experimental thermal conductivity data.

Pressure dependence of $ppa_l$ explained by increased four-phonon scattering.

Machine learning force field accurately captures pressure effects.

Abstract

Accurate prediction of lattice thermal conductivity ($\kappa_l$) in strongly anharmonic materials requires renormalized interatomic force constants (IFCs) and appropriate incorporation of diagonal and off-diagonal contributions and higher-order scattering. We investigate CuCl, a highly anharmonic system with a simple zincblende structure and ultralow $\kappa_l$. Our calculations, including IFC renormalization and four-phonon scattering, show excellent agreement with the experiment, underscoring the critical role of both effects in the accurate estimation of $\kappa_l$. Furthermore, the unusual pressure dependence of $\kappa_l$ is explored using a rigorously validated machine-learned force field, with the predicted values showing good agreement with the experimentally observed trend of monotonic decrease. This behavior is primarily driven by a significant increase in four-phonon scattering and a reduction in the group velocity of transverse acoustic modes. Overall, this study establishes a robust framework for modeling thermal transport in strongly anharmonic materials.