Accelerating prediction of phonon thermal conductivity by an order of magnitude through machine learning assisted extraction of anharmonic force constants

The calculation of material phonon thermal conductivity from density functional theory calculations requires the computationally expensive evaluation of anharmonic interatomic force constants and has remained a computational bottleneck in the high-throughput discovery of materials. In this paper, we present a machine learning assisted approach for the extraction of anharmonic force constants through local learning of the potential energy surface. We demonstrate our approach on a diverse collection of 220 ternary materials for which the total computational time for anharmonic force constant evaluation is reduced by more than an order of magnitude from 480 000 CPU hours to less than 12 000 CPU hours while preserving the thermal conductivity prediction accuracy to within 10%. Our approach removes a major hurdle in computational thermal conductivity evaluation and will pave the way forward for the high-throughput discovery of materials.