# Predicting interfacial thermal resistance by machine learning

https://mdr.nims.go.jp/datasets/c3c7ffd1-722c-4db5-97c7-980367f9c67a

## File

- [Wu_et_al-2019-npj_Computational_Materials.pdf](https://mdr.nims.go.jp/filesets/ed54eec7-56a9-426a-9fdd-aae05e65de56/download) ([Detail](https://mdr.nims.go.jp/filesets/ed54eec7-56a9-426a-9fdd-aae05e65de56.md))

## Id

c3c7ffd1-722c-4db5-97c7-980367f9c67a

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2023-03-13T08:01:00.381765Z

## Updated at

2024-01-05T13:13:21.520550Z

## Published at

2023-03-20T07:13:30.756074Z

## Doi



## First published url

https://doi.org/10.1038/s41524-019-0193-0

## Date published

2019-05-03

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Predicting interfacial thermal resistance by machine learning
  title_type: original
  lang: en

## Description

- description: 'Lots of factors affect the interfacial thermal resistance (ITR), making
    ITR prediction a high-dimensional mathematical problem. Machine learning is a
    cost-effective method to address this high-dimensional problem. The ITR predictive
    model is based on experimental data. The physical, chemical, and material properties
    of ITR are categorized into three sets of descriptors: property descriptors, compound
    descriptors, and process descriptors. There are three algorithms used for the
    models, support vector machines (SVMs), Gaussian Regression Processes (GRPs) and
    Regression tree ensembles of LSBoost (simplified as LSBoost). Those descriptors
    assist all the models in reducing the mismatch between predicted and experimental
    values and reaching high predictive performance of 96%. Over 80 thousand material
    systems composed of 293 kinds of materials (including elements or binary compounds)
    were input for prediction. Among the top-100 high-ITR predictions by the three
    different algorithms, 25 material systems are repeatedly predicted by at least
    two algorithms. One of the 25 material systems, Bi/Si, was processed and achieved
    the ultra-low thermal conductivity in our previous work. We believe that the high-ITR
    material systems from the prediction can be the potential candidates for thermoelectric
    application. This study proposed a strategy of material systems exploration for
    thermal management by means of machine learning.'
  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: Lei Fang
  role: author
  orcid: https://orcid.org/0000-0003-4706-0521
  organization: National Institute for Materials Science
  ror: https://ror.org/026v1ze26
- 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, thermal management
  schema: not_defined

## Rights

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

## Other identifier(s)



## Data origin



## Embargo



## Journal

- title: npj Computational Materials
  issn: '20573960'
  volume: '5'
  issue: '1'
  start_page: 56
  end_page: 56

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## Instrument



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## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



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## Fileset

- id: ed54eec7-56a9-426a-9fdd-aae05e65de56
  filename: Wu_et_al-2019-npj_Computational_Materials.pdf
  content_type: application/pdf
  size: 1604029
  md5: c1396655038e7e4ff535b8008f6da06a

## Thumbnail

fileset_id: ed54eec7-56a9-426a-9fdd-aae05e65de56
filename: Wu_et_al-2019-npj_Computational_Materials.pdf