# Designing Thermal Insulating thin films with Data-Driven Innovation

https://mdr.nims.go.jp/datasets/77e58aca-09b5-49b1-92b1-94da39bbe8ee

## File

- [Abstract_YenJu Wu.docx](https://mdr.nims.go.jp/filesets/14e111f3-601a-446a-8c32-6a0c2bfc43c9/download) ([Detail](https://mdr.nims.go.jp/filesets/14e111f3-601a-446a-8c32-6a0c2bfc43c9.md))

## Id

77e58aca-09b5-49b1-92b1-94da39bbe8ee

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## Visibility

open_to_public

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published

## Created at

2025-03-11T08:38:50.567192Z

## Updated at

2025-03-12T07:30:31.803239Z

## Published at

2025-03-12T07:30:31.924392Z

## Doi

https://doi.org/10.48505/nims.5363

## First published url



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## Resource type

conference_presentation

## Manuscript type

authors_original

## Collection



## Title

- title: Designing Thermal Insulating thin films with Data-Driven Innovation
  title_type: original
  lang: en

## Description

- description: Thermal insulating thin films are essential for advanced energy and
    thermal management applications. This presentation focuses on the integration
    of experimental data and machine learning techniques to design and optimize high-performance
    thermal insulating thin films. A key aspect of this work is the development of
    predictive models for interfacial thermal resistance (ITR), which combine experimental
    databases and machine learning to accurately predict heat flow across material
    interfaces. Case studies will demonstrate how these models are applied to improve
    thin-film thermal insulators and identify structural features that enhance their
    performance. The presentation also explores the extension of these data-driven
    approaches to amorphous materials, focusing on the structural analysis of amorphous
    germanium. We uncover the structural factors influencing thermal conductivity
    in disordered systems using experimental data from frequency-domain thermoreflectance
    and high-resolution microscopy. These findings illustrate how combining experimental
    databases with advanced computational techniques not only accelerates the development
    of thermal insulating materials but also deepens our understanding of heat transport
    in complex systems. This work bridges experimental and computational methodologies
    to innovate thermal insulation technologies, offering solutions for energy-efficient
    and sustainable materials.
  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
  department: Center for Basic Research on Materials/Data-driven Materials Research
    Field/Data-driven Inorganic Materials Group
- name: Yibin Xu
  role: author
  orcid: https://orcid.org/0000-0001-8600-8748
  organization: National Institute for Materials Science
  department: Center for Basic Research on Materials/Data-driven Materials Research
    Field/Data-driven Inorganic Materials Group

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## Keyword

- subject: Interfacial thermal resistance
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: thermal insulator
  schema: not_defined
- subject: thin film
  schema: not_defined

## Rights

- identifier: http://rightsstatements.org/vocab/InC/1.0/

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## Data origin

- data_origin_type: other

## Embargo



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## Conference

name: Nature Conferences-Materials for AI, AI for Materials
start_date: 2025-02-05
end_date: 2025-02-07
identifier: https://communities.springernature.com/posts/materials-for-ai-ai-for-materials

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## Fileset

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  filename: Abstract_YenJu Wu.docx
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  size: 16169
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## Thumbnail

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filename: Abstract_YenJu Wu.docx