Torres Pol, Wu Stephen, Ju Shenghong, Liu Chang, Tadano Terumasa, Yoshida Ryo, Shiomi Junichiro
Department of Mechanical Engineering, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, 113-8656, Japan.
EURECAT, Technology Center of Catalonia, Applied Artificial Intelligence, 08290 Cerdanyola, Barcelona, Spain.
J Phys Condens Matter. 2022 Jan 25;34(13). doi: 10.1088/1361-648X/ac49c9.
Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The hydrodynamic thermal transport is governed intrinsically by the hydrodynamic scale and the thermal conductivity. The correlations between these intrinsic properties and harmonic and anharmonic properties, and a large number of compositional (290) and structural (1224) descriptors of 131 crystal compound materials are obtained, revealing some of the key descriptors that determines the magnitude of the intrinsic hydrodynamic effects, most of them related with the phonon relaxation times. Then, a trained black-box model is applied to screen more than 5000 materials. The results identify materials with potential technological applications. Understanding the properties correlated to hydrodynamic thermal transport can help to find new thermoelectric materials and on the design of new materials to ease the heat dissipation in electronic devices.
机器学习技术被用于探索流体动力学热传输的内在起源，并寻找对科学和工程有意义的新材料。流体动力学热传输本质上由流体动力学尺度和热导率决定。获得了这些内在性质与谐波和非谐波性质之间的相关性，以及131种晶体复合材料的大量成分（290个）和结构（1224个）描述符，揭示了一些决定内在流体动力学效应大小的关键描述符，其中大多数与声子弛豫时间有关。然后，应用经过训练的黑箱模型筛选5000多种材料。结果确定了具有潜在技术应用的材料。理解与流体动力学热传输相关的性质有助于寻找新的热电材料，并有助于设计新材料以缓解电子设备中的热耗散。
