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

@article{Miyazaki2021MachineLB, title={Machine learning based prediction of lattice thermal conductivity for half-Heusler compounds using atomic information}, author={Hidetoshi Miyazaki and Tomoyuki Tamura and Masashi Mikami and Kosuke Watanabe and Naoki Ide and Osman Murat Ozkendir and Y. Nishino}, journal={Scientific Reports}, year={2021}, volume={11} }

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…

## 2 Citations

Lattice thermal conductivity of half-Heuslers with density functional theory and machine learning: Enhancing predictivity by active sampling with principal component analysis

- PhysicsComputational Materials Science
- 2022

Low lattice thermal conductivity is essential for high thermoelectric performance of a material. Lattice thermal conductivity is often computed based on density functional theory calculations, but…

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