キーワード: Machine learning
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27 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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takahara-et-al-2024-toward-the-atomic-level-analysis-of-ground-state-electronic-structures-of-solid-materials-via.pdf
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
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
ORCID SAMURAI ;
Koji Kimoto (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
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
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
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
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.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
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
Manuscript_final.pdf
Elemental Reactivity Maps for Materials Discovery
ジャーナル論文
著者
Yuki Inada (author) (この著者で検索)
ORCID ;
Masaya Fujioka (author) (この著者で検索)
ORCID ;
Haruhiko Morito (author) (この著者で検索)
;
Tohru Sugahara (author) (この著者で検索)
ORCID ;
Hisanori Yamane (author) (この著者で検索)
ORCID ; ORCID SAMURAI
キーワード
Crystal structure, Elements, Machine learning, Materials, Reactivity
刊行年月日
2025-03-25
更新時刻
2026-02-21 16:30:08 +0900
PhysRevMaterials.7.093805.pdf
Quasicrystals predicted and discovered by machine learning
ジャーナル論文
著者
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
ORCID SAMURAI ;
Alok Singh (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Erina Fujita (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Yukari Katsura (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
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
ORCID ;
Ryo Yoshida (author) (この著者で検索)
ORCID
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
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
1-s2.0-S135964542400692X-main.pdf
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
Cédric Bourgès (author) (この著者で検索)
National Institute for Materials Science
ORCID ;
Guillaume Lambard (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Naoki Sato (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Makoto Tachibana (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Satoshi Ishii (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Takao Mori (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI
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
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