Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach
Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach
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固有流体动力热传输的描述符：用机器学习方法筛选声子数据库
DOI：
10.1088/1361-648x/ac49c9
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Shiomi Junichiro
中科院分区：
文献类型：
--
作者：
Torres Pol;Wu Stephen;Ju Shenghong;Liu Chang;Tadano Terumasa;Yoshida Ryo;Shiomi Junichiro
机器学习技术用于探索流体动力学热传输的内在起源，并发现科学和工程感兴趣的新材料。流体动力学热输运本质上由流体动力学尺度和热导率决定。得到了131种晶体化合物材料的内禀性质与谐波和非谐波性质之间的相关性，以及大量的成分（290）和结构（1224）描述符，揭示了决定内禀流体力学效应大小的一些关键描述符，其中大部分与声子弛豫时间有关。然后，一个经过训练的黑盒模型被应用于筛选5000多个材料。结果确定具有潜在技术应用的材料。了解与流体动力学热传输相关的性质可以帮助发现新的热电材料，并设计新材料以减轻电子设备中的散热。
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.