Integrating machine learning with advanced processing and characterization for polycrystalline materials: a methodology review and application to iron-based superconductors
In this review, we present a new set of machine learning-based materials research methodologies for polycrystalline materials developed through the Core Research for Evolutionary Science and Technology project of the Japan Science and Technology Agency. We focus on the constituents of polycrystallin...
Saved in:
| Main Authors: | Akiyasu Yamamoto, Akinori Yamanaka, Kazumasa Iida, Yusuke Shimada, Satoshi Hata |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Science and Technology of Advanced Materials |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/14686996.2024.2436347 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Superstrength permanent magnets with iron-based superconductors by data- and researcher-driven process design
by: Akiyasu Yamamoto, et al.
Published: (2024-06-01) -
Uncertainty Quantification and Assimilation Method Based on Evaluated Nuclear Data Sampling Method
by: HUANG Yihan, ZU Tiejun, LIU Zhengming, CAO Liangzhi, WU Hongchun
Published: (2025-06-01) -
Scalable data assimilation with message passing
by: Oscar Key, et al.
Published: (2025-01-01) -
TEEMLEAP—A New Testbed for Exploring Machine Learning in Atmospheric Prediction for Research and Education
by: J. Wilhelm, et al.
Published: (2025-07-01) -
Application of PFI-4DVar Data Assimilation Technique to Nowcasting of Numerical Model
by: Jiang Wenjing, et al.
Published: (2020-09-01)