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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.

Rights:

• Creative Commons BY Attribution 4.0 International
  

Keyword: Superconducting material, Bayesian optimization, Process informatics, Phase purity

Date published: 2023-08-01

Publisher: Elsevier BV

Journal:

• Journal of Alloys and Compounds (ISSN: 09258388) vol. 966 171613

Funding:

• Core Research for Evolutional Science and Technology
• Japan Science and Technology Agency JPMJCR18J4

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1016/j.jallcom.2023.171613

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Updated at: 2026-08-27 16:31:22 +0900

Published on MDR: 2026-08-27 18:27:17 +0900