2025/08/21 by Ashis Kundu, Florian Knoop, Kundu, Ashis +3
Materials Science · #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Thermal Expansion and Ionic Conductivity #Thermal properties of materials
paper · pdf · doi:10.48550/arxiv.2508.15525
openalex publication_date 2025/08/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31
Accurate prediction of lattice thermal conductivity (κl) 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 κl. 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 κl. Furthermore, the unusual pressure dependence of κl 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.