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説明:

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.

権利情報:

• Creative Commons BY Attribution 4.0 International
  

キーワード: interfacial thermal resistance, machine learning, database, descriptor

刊行年月日: 2020-02-03

出版者: Springer Science and Business Media LLC

掲載誌:

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

研究助成金:

原稿種別: 出版者版 (Version of record)

MDR DOI:

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

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更新時刻: 2024-01-05 22:11:52 +0900

MDRでの公開時刻: 2023-03-20 16:12:27 +0900