# Networking autonomous material exploration systems through transfer learning

https://mdr.nims.go.jp/datasets/a6ea884d-b205-4a37-b424-86def8485177

## Files

- [s41524-025-01851-8 (1).pdf](https://mdr.nims.go.jp/filesets/812ff92d-bb9f-44e4-b004-78ef030e3229/download) ([Detail](https://mdr.nims.go.jp/filesets/812ff92d-bb9f-44e4-b004-78ef030e3229.md))

## Id

a6ea884d-b205-4a37-b424-86def8485177

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-12-12T06:18:28.225363Z

## Updated at

2025-12-12T23:30:26.007015Z

## Published at

2025-12-12T23:22:19.715511Z

## Doi



## First published url

https://doi.org/10.1038/s41524-025-01851-8

## Date published

2025-12-09

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Networking autonomous material exploration systems through transfer learning
  title_type: original
  lang: en

## Description

- description: Autonomous material exploration systems that integrate robotics, material
    simulations, and machine learning have advanced rapidly in recent years. Although
    their number continues to grow, these systems currently operate in isolation,
    limiting the overall efficiency of autonomous material discovery. In analogy to
    how human researchers advance materials science by sharing knowledge and collaborating,
    autonomous systems can also benefit from networking and knowledge exchange. Here,
    we propose a framework in which multiple autonomous material exploration systems
    form a network via transfer learning, selectively utilizing relevant knowledge
    from other systems in real time. We demonstrate this approach using three distinct
    autonomous systems and show that such networking significantly enhances the efficiency
    of material discovery. Our results suggest that the proposed framework can enable
    the development of large-scale autonomous material exploration networks, ultimately
    accelerating progress in material development.
  description_type: abstract
  lang: und

## Creator

- name: Naoki Yoshida
  role: author
- name: Yutaro Iwabuchi
  role: author
- name: Yasuhiko Igarashi
  role: author
- name: Yuma Iwasaki
  role: author
  orcid: https://orcid.org/0000-0002-7117-277X
  organization: National Institute for Materials Science

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

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## Keyword

- subject: machine learning
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/
  date_licensed: 2025-12-09

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: npj Computational Materials
  issn: '20573960'
  volume: '11'
  issue: '1'

## Conference



## Related item



## Funding

- identifier: JPMJCR21O1
  funder_name: JST-CREST
- identifier: JPMJCR21O1
  funder_name: JST-CREST
- identifier: JPMJCR21O1
  funder_name: JST-CREST
- identifier: JPMJCR21O1
  funder_name: JST-CREST

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## Fileset

- id: 812ff92d-bb9f-44e4-b004-78ef030e3229
  filename: s41524-025-01851-8 (1).pdf
  content_type: application/pdf
  size: 2234810
  md5: 1d9faa5cfe2c8a245826aa6da9c05581

## Thumbnail

fileset_id: 812ff92d-bb9f-44e4-b004-78ef030e3229
filename: s41524-025-01851-8 (1).pdf