# Development of Premix Internal-Magnesium-Diffusion MgB                    <sub>2</sub>                    Wire Using a Data-Driven Approach

https://mdr.nims.go.jp/datasets/9c402318-b1d4-405c-aeb1-14f66701078f

## File

- [AMatsumoto_final.pdf](https://mdr.nims.go.jp/filesets/8dcab28c-a97f-4408-95cd-f54a4ce309ca/download) ([Detail](https://mdr.nims.go.jp/filesets/8dcab28c-a97f-4408-95cd-f54a4ce309ca.md))

## Id

9c402318-b1d4-405c-aeb1-14f66701078f

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-01-29T07:01:38.494951Z

## Updated at

2026-01-30T07:30:04.825130Z

## Published at

2026-01-30T04:53:18.301794Z

## Doi

https://doi.org/10.48505/nims.6169

## First published url

https://doi.org/10.1109/tasc.2026.3652546

## Date published

2026-01-14

## Recorded date published



## Resource type

journal_article

## Manuscript type

accepted_manuscript

## Collection



## Title

- title: Development of Premix Internal-Magnesium-Diffusion MgB                    <sub>2</sub>                    Wire
    Using a Data-Driven Approach
  title_type: original
  lang: en

## Description

- description: Optimizing various fabrication parameters is one of the major challenges
    in the development of superconducting wires, often resulting in prolonged transition
    periods from fundamental research to practical applications. Additionally, escalating
    costs for essential resources such as liquid helium have amplified the difficulty
    of experimental work, further underscoring the importance of data-driven research
    approaches. In this study, we focus on magnesium diboride (MgB₂) wires and demonstrate
    the effectiveness of Bayesian optimization in efficiently searching complex parameter
    spaces to identify optimal fabrication conditions. Specifically, we investigate
    the internal magnesium diffusion (IMD) process, employing Bayesian optimization
    and the BOXVIA visualization tool to explore key heat-treatment parameters—namely,
    heat-treatment time and temperature—with the aim of maximizing the engineering
    critical current density (Je). Our results show that, under conventional conditions,
    the highest Je was achieved at approximately 700 °C with a short holding time
    of less than one hour. Moreover, our process informatics approach enabled the
    discovery of optimal conditions even under unconventional parameter settings.
    This methodology substantially reduces the number of experimental iterations required
    and enhances the performance of superconducting wires. Overall, our data-driven
    optimization strategy offers a promising route for faster, more efficient wire
    fabrication and the accelerated commercialization of superconducting technologies.
  description_type: abstract
  lang: und

## Creator

- name: Akiyoshi Matsumoto
  role: author
  orcid: https://orcid.org/0000-0002-6388-2130
- name: Akimitsu Ishii
  role: author
  orcid: https://orcid.org/0000-0002-9261-4047
- name: Rei Kawasaki
  role: author
- name: Takahiro Hosokawa
  role: author
- name: Akiyasu Yamamoto
  role: author
  orcid: https://orcid.org/0000-0001-6346-3422

## Contact agent



## Publisher

organization: Institute of Electrical and Electronics Engineers (IEEE)

## Managing organization



## Keyword

- subject: Internal magnesium diffusion
  schema: not_defined
- subject: Process informatics
  schema: not_defined
- subject: Bayesian optimization
  schema: not_defined
- subject: MgB2
  schema: not_defined
- subject: Premix-IMD
  schema: not_defined

## Rights

- description: "© 2026 IEEE.  Personal use of this material is permitted.  Permission
    from IEEE must be obtained for all other uses, in any current or future media,
    including reprinting/republishing this material for advertising or promotional
    purposes, creating new collective works, for resale or redistribution to servers
    or lists, or reuse of any copyrighted component of this work in other works."
  identifier: http://rightsstatements.org/vocab/InC/1.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: IEEE Transactions on Applied Superconductivity
  issn: '10518223'
  volume: '36'
  issue: '5'
  start_page: 1
  end_page: 4

## Conference



## Related item



## Funding

- funder_name: National Institute for Materials Science
- funder_name: Advanced Research Infrastructure for Materials and Nanotechnology in
    Japan
- identifier: JPMJCR18J4
  funder_name: Ministry of Education, Culture, Sports, Science and Technology
- identifier: JP21H01615
  funder_name: JSPS KAKENHI

## Instrument



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## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



## Structural feature for specimen



## Specific property for specimen



## Process for specimen treatment



## Computational method



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## Fileset

- id: 8dcab28c-a97f-4408-95cd-f54a4ce309ca
  filename: AMatsumoto_final.pdf
  content_type: application/pdf
  size: 832935
  md5: 2c723e4106587862085d5836365959e3

## Thumbnail

fileset_id: 8dcab28c-a97f-4408-95cd-f54a4ce309ca
filename: AMatsumoto_final.pdf