Machine learning prediction of thermal and elastic properties of double half-Heusler alloys

Double half-Heusler alloys are promising materials for applications as magnetocaloric materials, topological insulators, but especially thermoelectric materials. Four different elements in their composition provide a wide range of possible compositions, which, on the other hand, is difficult to study directly by applying traditional first-principles approaches to large number of compositions. In this work, based on the gradient boosting method, regression models are constructed that allow rapid prediction of the lattice thermal conductivity, as well as a number of other thermal and elastic properties, based on the composition and crystal structure of a compound. This made it possible for the first time to calculate the lattice thermal conductivity, as well as Gruneisen parameter, Debye temperature, and elastic moduli for a number of double half-Heusler compounds. We observe that the predicted thermal conductivity is in better agreement with the experimental data than the results of density functional theory calculations available in the literature. Half-Heusler compounds with thermal con-ductivity values lower than those previously known have been found. In addition, we have analyzed the importance of various features for predicting each of the studied properties, and the effect of the crystallographic symmetry of the compound on the prediction accuracy.

Automated summary

Based on the gradient boosting method, regression models are constructed to rapidly predict the lattice thermal conductivity and other thermal and elastic properties of double half-Heusler compounds. These models have allowed for the calculation of properties that were previously difficult to study directly. The results show good agreement with experimental data.