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Description:

Heat transfer at interfaces plays a critical role in material design and device performance. Higher interfacial thermal resistances (ITRs) affect the device efficiency and increase the energy consumption. Conversely, higher ITRs can enhance the figure of merit of thermoelectric materials by achieving ultra-low thermal conductivity via nanostructuring. This study proposes a dataset of descriptors for predicting the ITRs. The dataset includes two parts: one part consists of ITRs data collected from 87 experimental papers and the other part consists of the descriptors of 289 materials, which can construct over 80,000 pair-material systems for ITRs prediction. The former part is composed of over 1300 data points of metal/nonmetal, nonmetal/nonmetal, and metal/metal interfaces. The latter part consists of physical and chemical properties that are highly correlated to the ITRs. The synthesis method of the materials and the thermal measurement technique are also recorded in the dataset for further analyses. These datasets can be applied not only to ITRs predictions but also to thermal-property predictions or heat transfer on various material systems.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: interfacial thermal resistance, machine learning, database, descriptor

Date published: 2020-02-03

Publisher: Springer Science and Business Media LLC

Journal:

• Scientific Data (ISSN: 20524463) vol. 7 issue. 1 p. 36-36

Funding:

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1038/s41597-020-0373-2

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Updated at: 2024-01-05 22:11:52 +0900

Published on MDR: 2023-03-20 16:12:27 +0900