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 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.
Highlights
Half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology
It is one of the most fundamental and important physical quantities. It is an important physical quantity in terms of application, which is necessary for the understanding of thermal management to ensure the performance, life-time, and safety for thermoelectric energy conversion devices, and spintronics technology
The prediction of thermal conductivity using non-equilibrium molecular dynamics requires an enormous amount of computational time because of the need to calculate the time evolution of the vast amount of atomic movements
Summary
Half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. Theoretical predictions of the lattice thermal conductivity of solids can be made using non-equilibrium molecular dynamics simulations[1,2,3,4,5,6,7] or the density functional theory (DFT) calculations[8,9,10,11,12,13,14,15,16]. Carrete et al.[41] and Liu et al.[42] attempted to predict the lattice thermal conductivity of half-Heusler compounds obtained from the results of the DFT calculations by ML. Carrete et al reported that the lattice thermal conductivity of half-Heusler compounds can be predicted in the range of 10% using the Young’s modulus value obtained from the DFT calculations as the descriptor for the ML. Liu et al reported that the lattice thermal conductivity of half-Heusler compounds can be predicted in the range of 10% using the atomic numbers, atomic masses, and atomic radii of the constituent atoms of half-Heusler compounds as the descriptors for ML
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