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

• Published in: Scientific reports 2021-06, Vol.11 (1), p.13410-13410, Article 13410
• Main Authors: Miyazaki, Hidetoshi, Tamura, Tomoyuki, Mikami, Masashi, Watanabe, Kosuke, Ide, Naoki, Ozkendir, Osman Murat, Nishino, Yoichi
• Format: Article
• Language: English
• Subjects: 639/301/1034/1037; 639/301/119/1002; Crystal structure; Energy conversion; Heat conductivity; Humanities and Social Sciences; Learning algorithms; Machine learning; multidisciplinary; Predictions; Science; Science (multidisciplinary); Thermal conductivity
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-92030-4

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.