Quasicrystals predicted and discovered by machine learning
- ジャーナル論文
- 著者
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;Chang Liu (author) (この著者で検索);Koichi Kitahara (author) (この著者で検索);Asuka Ishikawa (author) (この著者で検索)Takanobu Hiroto (author) (この著者で検索)National Institute for Materials Science
;
Alok Singh (author) (この著者で検索)National Institute for Materials Science
;
Erina Fujita (author) (この著者で検索)National Institute for Materials Science
;
Yukari Katsura (author) (この著者で検索)National Institute for Materials Science
;
;Yuki Inada (author) (この著者で検索);Ryuji Tamura (author) (この著者で検索)Kaoru Kimura (author) (この著者で検索)National Institute for Materials Science
;
Ryo Yoshida (author) (この著者で検索)
https://orcid.org/0000-0001-8092-0162
(unauthenticated)
- キーワード
- Crystal forms, Crystal phenomena, Crystal structure, Alloys, Machine learning
- 刊行年月日
- 2023-09-25
- 更新時刻
- 2024-11-21 16:36:03 +0900
Data-driven analysis of hydrogen embrittlement in martensitic steels with interpretable machine learning
- ジャーナル論文
- 著者
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Houichi Kitano (author) (この著者で検索)
;
Yuuji Kimura (author) (この著者で検索)
;
Akinobu Shibata (author) (この著者で検索)
- キーワード
- Hydrogen embrittlement, Martensitic steel, Machine learning, hap (shapley additive explanations), Symbolic regression
- 刊行年月日
- 2026-04-23
- 更新時刻
- 2026-06-17 09:57:29 +0900
A straightforward gradient-based approach for designing superconductors with high critical temperature: exploiting domain knowledge via adaptive constraints
- ジャーナル論文
- 著者
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;Akihiro Fujii (author) (この著者で検索)Anh Khoa Augustin Lu (author) (この著者で検索)National Institute for Materials Science
;
;Koji Shimizu (author) (この著者で検索)Satoshi Watanabe (author) (この著者で検索)
https://orcid.org/0000-0002-8069-6938
(unauthenticated)
National Institute for Materials Science
- キーワード
- Gradient-based optimization, Superconductor, Domain knowledge integration, Materials discovery, Critical temperature, Machine learning
- 刊行年月日
- 2025-10-29
- 更新時刻
- 2026-05-18 14:53:14 +0900