  The half-Heusler compound has drawn attention in a variety of fields as a
candidate material for thermoelectric energy conversion and spintronics
technology. This is because it has various electronic structures, such as
semi-metals, semiconductors, and a topological insulator. 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, which is an important physical parameter for controlling the
thermal management of the device, requires a calculation cost of several 100
times as much as the usual density functional theory calculation. Therefore, 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
calculation 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.
