キーワード: Machine learning
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13 件のレコードが見つかりました。 1件目から10件目まで表示します。

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Wu 2025 J Appl Crystallogr nanoXRD UMAP HVPE GaN.pdf
Machine learning assisted nanobeam X-ray diffraction based analysis on hydride vapor-phase epitaxy GaN
ジャーナル論文
著者
Zhendong Wu (author) (この著者で検索)
;
Yusuke Hayashi (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Tetsuya Tohei (author) (この著者で検索)
;
Kazushi Sumitani (author) (この著者で検索)
;
Yasuhiko Imai (author) (この著者で検索)
;
Shigeru Kimura (author) (この著者で検索)
;
Akira Sakai (author) (この著者で検索)
キーワード
GaN, SPring-8, nanoXRD, Machine learning
刊行年月日
2025-08-01
更新時刻
2025-07-31 12:30:18 +0900
abstract_YOSHIKAWA.pdf
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
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
ORCID SAMURAI ;
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
Manuscript.pdf
Data-driven optimization of FePt heat-assisted magnetic recording media accelerated by deep learning TEM image segmentation
ジャーナル論文
著者
N. Kulesh (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
A. Bolyachkin (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
I. Suzuki (author) (この著者で検索)
National Institute for Materials Science
ORCID ;
Y.K. Takahashi (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
H. Sepehri-Amin (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
K. Hono (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI
キーワード
Heat-assisted magnetic recording (HAMR), FePt, Deep learning, Machine learning, Image segmentation
刊行年月日
2023-05-27
更新時刻
2025-05-27 08:30:20 +0900
main.pdf
Data-driven analysis of hydrogen embrittlement in martensitic steels with interpretable machine learning
ジャーナル論文
著者
ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI
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
Manuscript.pdf
In Situ Forming Supramolecular Nanofiber Hydrogel as a Biodegradable Liquid Embolic Agent for Postembolization Tissue Remodeling
ジャーナル論文
著者
Akihiro Nishiguchi (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Miho Ohta (author) (この著者で検索)
;
Debabrata Palai (author) (この著者で検索)
National Institute for Materials Science
ORCID ;
Shima Ito (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Kensaku Mori (author) (この著者で検索)
;
Ryotaro Akagi (author) (この著者で検索)
;
Christophe Bajan (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Guillaume Lambard (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Keitaro Sodeyama (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Tetsushi Taguchi (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI
キーワード
Hydrogel, Embolic agent, Machine learning
刊行年月日
2024-11-07
更新時刻
2025-02-26 16:30:29 +0900
d3dd00215b.pdf
Automated odor-blending with one-pot Bayesian optimization
ジャーナル論文
著者
Yota Fukui (author) (この著者で検索)
;
Kosuke Minami (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Kota Shiba (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Genki Yoshikawa (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Koji Tsuda (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Ryo Tamura (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI
キーワード
Machine learning, MSS, Bayesian optimization, Olfactory sensor
刊行年月日
2024-04-16
更新時刻
2024-06-10 12:30:19 +0900
D5DD00437C.pdf
Automated synthesis and fragment descriptor-based machine learning for retention time prediction in supercritical fluid chromatography
ジャーナル論文
著者
Sitanan Sartyoungkul (author) (この著者で検索)
;
Balasubramaniyan Sakthivel (author) (この著者で検索)
;
Pavel Sidorov (author) (この著者で検索)
ORCID ;
Yuuya Nagata (author) (この著者で検索)
ORCID
キーワード
Automated synthesis, Fragment descriptor, Machine learning, Retention time prediction, Supercritical fluid chromatography
刊行年月日
2025-11-26
更新時刻
2025-12-24 15:05:55 +0900
manuscript.docx
Data-Driven Development of Magnetic Materials
プレゼンテーション
著者
BOLYACHKIN Anton (author) (この著者で検索)
National Institute for Materials Science Global Networking Division/International Center for Young Scientists
ORCID SAMURAI ;
SEPEHRI AMIN Hossein (author) (この著者で検索)
National Institute for Materials Science Research Center for Magnetic and Spintronic Materials/Magnetic Materials Analysis Group
ORCID SAMURAI ;
OHKUBO Tadakatsu (author) (この著者で検索)
National Institute for Materials Science Research Center for Magnetic and Spintronic Materials
ORCID SAMURAI
キーワード
Nd-Fe-B magnets, Magnetocaloric materials, Magnetic recodring, Machine learning
刊行年月日
更新時刻
2024-10-16 16:30:20 +0900
Fujii-DigitalDiscovery2025.pdf
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
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
ORCID SAMURAI ;
Koji Shimizu (author) (この著者で検索)
;
Satoshi Watanabe (author) (この著者で検索)
National Institute for Materials Science
ORCID
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
1-s2.0-S092702562600087X-main.pdf
Active learning for predicting the enthalpy of mixing in binary liquids based on ab initio molecular dynamics
ジャーナル論文
著者
Quentin Bizot (author) (この著者で検索)
ORCID ; ORCID SAMURAI ;
Guillaume Deffrennes (author) (この著者で検索)
ORCID
キーワード
CALPHAD, Machine learning
刊行年月日
2026-02-07
更新時刻
2026-04-01 13:44:57 +0900
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