Process parameters and magnetic properties (coercivity, remanence, squareness, maximum energy product) of data-driven fabrication of Nd-Fe-B anisotropic magnets by direct hot extrusion.
- データセット
- 著者
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Lambard, Guillaume (author) (この著者で検索)
;
Sasaki, Taisuke (author) (この著者で検索)
;
Sodeyama, Keitaro (author) (この著者で検索)
;
Ohkubo, Tadakatsu (author) (この著者で検索)
;
Hono, Kazuhiro (author) (この著者で検索)
- キーワード
- Maximum energy product, Coercivity, Squareness, Machine learning, Remanence, Process, Bayesian Optimization, Hot extrusion, Active learning, Nd-Fe-B magnet
- 刊行年月日
- 2021-10-11
- 更新時刻
- 2022-10-03 02:01:08 +0900
From Random Networks to AI-Driven Glass Design
- プレゼンテーション
- 著者
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N. M. Anoop Krishnan (author) (この著者で検索)Indian Institute of Technology Delhi Department of Civil Engineering
- キーワード
- Machine learning, AI-Driven Glass Design
- 刊行年月日
- 更新時刻
- 2025-09-25 16:30:42 +0900
Estimation of valence state and growth rate using principal component analysis of plasma emission in reactive sputtering deposition
- ジャーナル論文
- 著者
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;Rintaro Minami (author) (この著者で検索)University of Tsukuba a Department of Applied Physics;Eiji Kita (author) (この著者で検索);Chiharu Mitsumata (author) (この著者で検索)Hideto Yanagihara (author) (この著者で検索)
- キーワード
- Machine learning, principal component analysis (PCA), reactive magnetron sputtering, plasma emission spectrum, Mössbauer spectroscopy
- 刊行年月日
- 2025-12-31
- 更新時刻
- 2025-09-10 16:30:38 +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
ALMLBO PIPELINE APPROACH APPLY TO MATERIAL PROCESS AND COMPOSITION OPTIMIZATION FOR ENERGY-SAVING APPLICATIONS
- プレゼンテーション
- 著者
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Cédric Bourgès (author) (この著者で検索)National Institute for Materials Science International Center for Young Scientists
;
Guillaume Lambard (author) (この著者で検索)National Institute for Materials Science Center for Basic Research on Materials/Data-driven Materials Research Field/Data-driven Materials Design Group
;
Naoki Sato (author) (この著者で検索)National Institute for Materials Science Research Center for Materials Nanoarchitectonics (MANA)/Nanomaterials Field/Thermal Energy Materials Group
;
Makoto Tachibana (author) (この著者で検索)National Institute for Materials Science Research Center for Materials Nanoarchitectonics (MANA)/Nanomaterials Field/Thermal Energy Materials Group
;
Satoshi Ishii (author) (この著者で検索)National Institute for Materials Science Research Center for Materials Nanoarchitectonics (MANA)/Nanomaterials Field/Optical Nanostructure Team
;
Takao Mori (author) (この著者で検索)National Institute for Materials Science Research Center for Materials Nanoarchitectonics (MANA)/Nanomaterials Field/Thermal Energy Materials Group
- キーワード
- Kesterite, Machine learning, Process, Thermoelectric, Ceramic
- 刊行年月日
- 更新時刻
- 2024-10-30 16:30:23 +0900
Machine Learning to Predict Multicellular Dynamics Driven by Concentrated Polymer Brush-Modified Cellulose Nanofibers
- プレゼンテーション
- 著者
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Chiaki Yoshikawa (author) (この著者で検索)National Institute for Materials Science Research Center for Macromolecules and Biomaterials/Macromolecules Field/Polymer Surfaces and Devices Team
;
Hiroshi Mamitsuka (author) (この著者で検索)
- キーワード
- Machine learning, Multicellular dynamics, Concentrated polymer brush, Cellulose nanofiber
- 刊行年月日
- 更新時刻
- 2025-11-06 12:30:42 +0900
Toward the Atomic-Level Analysis of Ground-State Electronic Structures of Solid Materials via Prediction from Core-Loss Spectra: A Computational Study in Si
- ジャーナル論文
- 著者
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;Izumi Takahara (author) (この著者で検索)Fumihiko Uesugi (author) (この著者で検索)National Institute for Materials Science
;
Koji Kimoto (author) (この著者で検索)National Institute for Materials Science
;
;Kiyou Shibata (author) (この著者で検索)Teruyasu Mizoguchi (author) (この著者で検索)
- キーワード
- Machine learning, ELNES, XANES, PDOS
- 刊行年月日
- 2024-08-15
- 更新時刻
- 2024-10-02 12:30:19 +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
Process optimization on kesterite-based ceramics for enhancing their thermoelectric performances assisted by active machine learning approach: A tool for metal-sulfide ceramics development
- ジャーナル論文
- 著者
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Cédric Bourgès (author) (この著者で検索)National Institute for Materials Science
;
Guillaume Lambard (author) (この著者で検索)National Institute for Materials Science
;
Naoki Sato (author) (この著者で検索)National Institute for Materials Science
;
Makoto Tachibana (author) (この著者で検索)National Institute for Materials Science
;
Satoshi Ishii (author) (この著者で検索)National Institute for Materials Science
;
Takao Mori (author) (この著者で検索)National Institute for Materials Science
- キーワード
- Kesterite, Machine learning, Process, Thermoelectric, Ceramic
- 刊行年月日
- 2024-08-29
- 更新時刻
- 2024-09-20 16:30:30 +0900
Efficient autonomous material search method combining ab initio calculations, autoencoder, and multi-objective Bayesian optimization
- ジャーナル論文
- 著者
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Yuma Iwasaki (author) (この著者で検索)
;
Hwang Jaekyun (author) (この著者で検索)
https://orcid.org/0000-0002-1792-0044
(unauthenticated)
;
Yuya Sakuraba (author) (この著者で検索)
;
Masato Kotsugi (author) (この著者で検索)
https://orcid.org/0000-0002-4841-1808
(unauthenticated)
;
Yasuhiko Igarashi (author) (この著者で検索)
https://orcid.org/0000-0003-1042-6657
(unauthenticated)
- キーワード
- autonomous materials search, ab-initio, Machine learning
- 刊行年月日
- 2022-12-31
- 更新時刻
- 2024-01-05 22:14:05 +0900