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Thermal Nanostructure Design by Materials Informatics

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Artificial Intelligence for Materials Science

Part of the book series: Springer Series in Materials Science ((SSMATERIALS,volume 312))

Abstract

Tuning thermal transport by nanostructures has garnered increasing attentions as thermal materials with either high or low thermal conductivities are of great use in a wide range of applications like thermal management, thermal barriers, and thermoelectrics. Due to the superhigh degree of freedoms in terms of atom types and structural configurations, traditional searching algorithm may be powerless to find the optimal nanostructures with limited time and computation expenses, and thus the big-data-driven materials informatics (MI), as the fourth paradigm, has emerged and become prevalent. In this chapter, beginning with the brief introduction of the MI algorithms, we emphasize on the progress on MI-based thermal nanostructure designs, ranging from heat conduction through Si/Ge and GaAs/AlAs superlattices, graphene nanoribbons, to thermal emission for radiative cooling, ultranarrow emission, thermophotovoltaic system, and thermal camouflage. The remaining challenges and opportunities in this field are outlined and prospected.

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Authors and Affiliations

  1. School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan, China

    Run Hu

  2. Department of Mechanical Engineering, University of Tokyo, Tokyo, Japan

    Junichiro Shiomi

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  1. Run Hu
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Correspondence to Junichiro Shiomi .

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Editors and Affiliations

  1. Monash Suzhou Research Institute, Suzhou, China

    Yuan Cheng

  2. Institute of Artificial Intelligence, Beihang University, Beijing, China

    Tian Wang

  3. Institute of High Performance Computing, Singapore, Singapore

    Gang Zhang

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Hu, R., Shiomi, J. (2021). Thermal Nanostructure Design by Materials Informatics. In: Cheng, Y., Wang, T., Zhang, G. (eds) Artificial Intelligence for Materials Science. Springer Series in Materials Science, vol 312. Springer, Cham. https://doi.org/10.1007/978-3-030-68310-8_7

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