Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information

Half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. When the half-Heusler compound is incorporated into the device, the control of high lattice thermal conductivity owing to high crystal symmetry is a challenge for the thermal manager of the device. The calculation for the prediction of lattice thermal conductivity is an important physical parameter for controlling the thermal management of the device. We examined whether lattice thermal conductivity prediction by machine learning was possible on the basis of only the atomic information of constituent elements for thermal conductivity calculated by the density functional theory in various half-Heusler compounds. Consequently, we constructed a machine learning model, which can predict the lattice thermal conductivity with high accuracy from the information of only atomic radius and atomic mass of each site in the half-Heusler type crystal structure. Applying our results, the lattice thermal conductivity for an unknown half-Heusler compound can be immediately predicted. In the future, low-cost and short-time development of new functional materials can be realized, leading to breakthroughs in the search of novel functional materials.

Automated summary

Half-Heusler compounds have gained attention as candidate materials for thermoelectric energy conversion and spintronics technology. The challenge lies in controlling high lattice thermal conductivity due to high crystal symmetry in these compounds, but a machine learning model has been developed to accurately predict thermal conductivity from atomic information, aiding in the discovery of novel functional materials.