Machine learning approach for the prediction and optimization of thermal transport properties
Yulou Ouyang1, Cuiqian Yu1, Gang Yan1,2(), Jie Chen1()
1. Center for Phononics and Thermal Energy Science, China–EU Joint Lab for Nanophononics, School of Physics Science and Engineering, Tongji University, Shanghai 200092, China 2. Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai 200092, China
Traditional simulation methods have made prominent progress in aiding experiments for understanding thermal transport properties of materials, and in predicting thermal conductivity of novel materials. However, huge challenges are also encountered when exploring complex material systems, such as formidable computational costs. As a rising computational method, machine learning has a lot to offer in this regard, not only in speeding up the searching and optimization process, but also in providing novel perspectives. In this work, we review the state-of-the-art studies on material’s thermal properties based on machine learning technique. First, the basic principles of machine learning method are introduced. We then review applications of machine learning technique in the prediction and optimization of material’s thermal properties, including thermal conductivity and interfacial thermal resistance. Finally, an outlook is provided for the future studies.
. [J]. Frontiers of Physics, 2021, 16(4): 43200.
Yulou Ouyang, Cuiqian Yu, Gang Yan, Jie Chen. Machine learning approach for the prediction and optimization of thermal transport properties. Front. Phys. , 2021, 16(4): 43200.
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