Miyazaki Hidetoshi, Tamura Tomoyuki, Mikami Masashi, Watanabe Kosuke, Ide Naoki, Ozkendir Osman Murat, Nishino Yoichi
Department of Physical Science and Engineering, Nagoya Institute of Technology, Nagoya, 466-8555, Japan.
Frontier Research Institute for Materials Science, Nagoya Institute of Technology, Gokiso-cho, Showa-ku, Nagoya, 466-8555, Japan.
Sci Rep. 2021 Jun 28;11(1):13410. 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.
半赫斯勒化合物作为热电能量转换和自旋电子学技术的候选材料，在各个领域引起了关注。当将半赫斯勒化合物集成到器件中时，由于其高晶体对称性导致的高晶格热导率的控制，对器件的热管理来说是一项挑战。晶格热导率预测的计算是控制器件热管理的一个重要物理参数。我们基于密度泛函理论计算的各种半赫斯勒化合物的热导率，研究了仅根据组成元素的原子信息通过机器学习预测晶格热导率是否可行。结果，我们构建了一个机器学习模型，该模型可以从半赫斯勒型晶体结构中每个位点的原子半径和原子质量信息高精度地预测晶格热导率。应用我们的结果，可以立即预测未知半赫斯勒化合物的晶格热导率。未来，可以实现新型功能材料的低成本和短时间开发，从而在新型功能材料的探索方面取得突破。
