TY - JOUR

T1 - Machine learning prediction of thermal and elastic properties of double half-Heusler alloys

AU - Filanovich, A. N.

AU - Povzner, A. A.

AU - Lukoyanov, A. V.

N1 - This study was supported by the grant of Russian Science Foundation No 22-22-20109.

PY - 2023

Y1 - 2023

N2 - 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 Grüneisen 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 conductivity 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. © 2023 Elsevier B.V.

AB - 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 Grüneisen 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 conductivity 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. © 2023 Elsevier B.V.

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U2 - 10.1016/j.matchemphys.2023.128030

DO - 10.1016/j.matchemphys.2023.128030

M3 - Article

VL - 306

JO - Materials Chemistry and Physics

JF - Materials Chemistry and Physics

SN - 0254-0584

M1 - 128030

ER -