# Physical and chemical descriptors for predicting interfacial thermal resistance

https://mdr.nims.go.jp/datasets/37f96b88-6d52-43b0-8efd-b4ed225bd41f

## File

- [Wu_et_al-2020-Scientific_Data.pdf](https://mdr.nims.go.jp/filesets/8da92e34-e232-42c9-b5df-e9ade2175870/download) ([Detail](https://mdr.nims.go.jp/filesets/8da92e34-e232-42c9-b5df-e9ade2175870.md))

## Id

37f96b88-6d52-43b0-8efd-b4ed225bd41f

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-03-13T07:58:23.259149Z

## Updated at

2024-01-05T13:11:52.535282Z

## Published at

2023-03-20T07:12:27.934574Z

## Doi



## First published url

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

## Date published

2020-02-03

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Physical and chemical descriptors for predicting interfacial thermal resistance
  title_type: original
  lang: en

## Description

- 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.'
  description_type: abstract
  lang: eng

## Creator

- name: Yen-Ju Wu
  role: author
  orcid: https://orcid.org/0000-0003-2647-3407
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- name: Tianzhuo Zhan
  role: author
- name: Zhufeng Hou
  role: author
- name: Lei Fang
  role: author
- name: Yibin Xu
  role: author
  orcid: https://orcid.org/0000-0001-8600-8748
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: interfacial thermal resistance, machine learning, database, descriptor
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: Scientific Data
  issn: '20524463'
  volume: '7'
  issue: '1'
  start_page: 36
  end_page: 36

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## Instrument



## Instrument operator



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## Measurement method



## Specimen



## Chemical composition



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## Structural feature for specimen



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## Fileset

- id: 8da92e34-e232-42c9-b5df-e9ade2175870
  filename: Wu_et_al-2020-Scientific_Data.pdf
  content_type: application/pdf
  size: 2238012
  md5: f3f564536dc89a2d0198747981c36386

## Thumbnail

fileset_id: 8da92e34-e232-42c9-b5df-e9ade2175870
filename: Wu_et_al-2020-Scientific_Data.pdf