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… 
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