  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.
