Example of Data-driven Superconducting Wire Research

The data-driven research has progressed in recent years. Particularly in the field of superconducting materials, there has been active research into the search for new materials, especially by materials informatics (MI). On the other hand, in a superconducting material that has three critical points, the critical current density of the three critical points changes greatly depending on the material manufacturing process. The critical current density properties greatly depend on the material structure, and there are many parameters to obtain the appropriate mictrostructures for various applications. Therefore, these data will be useful to a wide range of users, including materials researchers and even superconducting wire suppliers and a field of application. In this commentary, we provided an overview of data collection, storage, and use, and then presented the progress of some ongoing research and discussed future development possibilities.

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Publication Details

Journal
IEEJ Transactions on Fundamentals and Materials
Published
2024-08-31
DOI
https://doi.org/10.1541/ieejfms.144.366
Citations
1
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.12
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article

Example of Data-driven Superconducting Wire Research

Masayoshi Inoue, A. Matsumoto, Shigeyuki Matsunami, Shinya Kawashima et al.
1 citations
IEEJ Transactions on Fundamentals and Materials
Machine Learning in Materials Science
0.12
article

Example of Data-driven Superconducting Wire Research

Masayoshi Inoue, A. Matsumoto, Shigeyuki Matsunami, Shinya Kawashima, Hiroshi Narazaki
article en
1 citations

Abstract

The data-driven research has progressed in recent years. Particularly in the field of superconducting materials, there has been active research into the search for new materials, especially by materials informatics (MI). On the other hand, in a superconducting material that has three critical points, the critical current density of the three critical points changes greatly depending on the material manufacturing process. The critical current density properties greatly depend on the material structure, and there are many parameters to obtain the appropriate mictrostructures for various applications. Therefore, these data will be useful to a wide range of users, including materials researchers and even superconducting wire suppliers and a field of application. In this commentary, we provided an overview of data collection, storage, and use, and then presented the progress of some ongoing research and discussed future development possibilities.

IEEJ Transactions on Fundamentals and MaterialsVol. 144(9)
Fukuoka Institute of Technology (JP), National Institute for Materials Science (JP)
Openalex Percentile: Top 60%
Machine Learning in Materials Science
0.12
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