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

ABSTRACT: 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 constructe

SUBMITTER: Miyazaki H 

PROVIDER: S-EPMC8239013 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature