# Application of Bayesian Optimization to the Synthesis Process of BaFe2(As,P)2 Polycrystalline Bulk Superconducting Materials

https://mdr.nims.go.jp/datasets/cb8bbe79-4ae9-4bd7-9fe5-ee865ceacded

## File

- [Application of Bayesian optimization to the synthesis process of BaFe2(As,P)2 polycrystalline bulk superconducting materials.pdf](https://mdr.nims.go.jp/filesets/62caaa99-9ec2-4e69-947b-b4d89611e31d/download) ([Detail](https://mdr.nims.go.jp/filesets/62caaa99-9ec2-4e69-947b-b4d89611e31d.md))

## Id

cb8bbe79-4ae9-4bd7-9fe5-ee865ceacded

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2026-08-27T03:46:11.137864Z

## Updated at

2026-08-27T07:31:22.368799Z

## Published at

2026-08-27T09:27:17.968319Z

## Doi



## First published url

https://doi.org/10.1016/j.jallcom.2023.171613

## Date published

2023-08-01

## Recorded date published

2023-12

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Application of Bayesian Optimization to the Synthesis Process of BaFe2(As,P)2
    Polycrystalline Bulk Superconducting Materials
  title_type: original
  lang: en

## Description

- description: This study is the first application of Bayesian optimization to the
    synthesis process of superconducting materials. As a model case, the phase purity
    of BaFe2(As,P)2 polycrystalline bulks, which affects their superconducting properties,
    was improved by optimizing only the heat-treatment temperature using Bayesian
    optimization. We determined the optimal temperature among 800 candidates in 13
    experiments, and a phase purity of 91.3 % was achieved. Moreover, the phosphorus
    doping level of the best sample approached the optimal doping level owing to a
    reduction in the impurity phase. Visualization of the Bayesian optimization process
    showed that a well-balanced global search and local optimization allowed us to
    obtain a rough correlation between the superconducting properties and experimental
    conditions and finely optimal experimental conditions over a wide range. These
    results demonstrate that Bayesian optimization is promising for optimizing the
    synthesis process of superconducting materials.
  description_type: abstract
  lang: und

## Creator

- name: Akimitsu Ishii
  role: author
  orcid: https://orcid.org/0000-0002-9261-4047
  organization: National Institute for Materials Science
- name: Shinjiro Kikuchi
  role: author
- name: Akinori Yamanaka
  role: author
- name: Akiyasu Yamamoto
  role: author

## Contact agent



## Publisher

organization: Elsevier BV

## Managing organization



## Keyword

- subject: Superconducting material
  schema: not_defined
- subject: Bayesian optimization
  schema: not_defined
- subject: Process informatics
  schema: not_defined
- subject: Phase purity
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/
  date_licensed: 2023-08-01

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Journal of Alloys and Compounds
  issn: '09258388'
  volume: '966'
  article_number: '171613'

## Conference



## Related item



## Funding

- funder_name: Core Research for Evolutional Science and Technology
- identifier: JPMJCR18J4
  funder_name: Japan Science and Technology Agency

## Instrument



## Instrument operator



## 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



## Energy level/transition state



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## Custom property



## Fileset

- id: 62caaa99-9ec2-4e69-947b-b4d89611e31d
  filename: Application of Bayesian optimization to the synthesis process of BaFe2(As,P)2
    polycrystalline bulk superconducting materials.pdf
  content_type: application/pdf
  size: 3853676
  md5: dd3fd390f27b68957ce8f43bf288884d

## Thumbnail

fileset_id: 62caaa99-9ec2-4e69-947b-b4d89611e31d
filename: Application of Bayesian optimization to the synthesis process of BaFe2(As,P)2
  polycrystalline bulk superconducting materials.pdf