キーワード: Machine learning
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31 件のレコードが見つかりました。 11件目から20件目まで表示します。

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Prediction_and_optimization_of_epoxy_adhesive_strength_from_a_small_dataset_through_active_learning.pdf
Prediction and optimization of epoxy adhesive strength from a small dataset through active learning
ジャーナル論文
著者
ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI ;
Pruksawan, Sirawit (author) (この著者で検索)
ORCID ; ORCID SAMURAI
キーワード
active learning, Machine learning, adhesive
刊行年月日
2019-12-31
更新時刻
2024-01-05 22:11:50 +0900
lin-et-al-2025-determination-of-stable-proton-configurations-by-black-box-optimization-using-an-ising-machine.pdf
Determination of Stable Proton Configurations by Black-Box Optimization Using an Ising Machine
ジャーナル論文
著者
Jianbo Lin (author) (この著者で検索)
;
Tomofumi Tada (author) (この著者で検索)
ORCID ;
Ai Koizumi (author) (この著者で検索)
;
Masato Sumita (author) (この著者で検索)
ORCID ; ORCID SAMURAI ; ORCID SAMURAI
キーワード
Machine learning, Ising machine, Proton configuration
刊行年月日
2025-02-06
更新時刻
2025-05-23 08:30:18 +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
manuscript.docx
Machine Learning Approach for Evaluation of Nanodefects and Magnetic Anisotropy in FePt Granular Films
ジャーナル論文
著者
E. Dengina (author) (この著者で検索)
National Institute for Materials Science
;
A. Bolyachkin (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
Magnetic recording, Micromagnetic simulations, Machine learning, FePt media
刊行年月日
2022-05-13
更新時刻
2024-10-15 16:30:13 +0900
abstract.docx
Machine Learning Assisted Optimization of SmFe12-based Alloys
会議発表ポスター
著者
BOLYACHKIN Anton (author) (この著者で検索)
National Institute for Materials Science International Center for Young Scientists
ORCID SAMURAI ;
SUBAGJA Toni (author) (この著者で検索)
National Institute for Materials Science Research Center for Magnetic and Spintronic Materials/Green Magnetic Materials Group
ORCID SAMURAI ;
LI Jiangnan (author) (この著者で検索)
National Institute for Materials Science Research Center for Magnetic and Spintronic Materials/Magnetic Materials Analysis Group
;
ZHANG Jiasheng (author) (この著者で検索)
National Institute for Materials Science Research Center for Magnetic and Spintronic Materials/Green Magnetic Materials Group
;
ASHOK KRISHNASWAMY Srinithi (author) (この著者で検索)
National Institute for Materials Science Research Center for Magnetic and Spintronic Materials/Green Magnetic Materials Group
ORCID ;
ABE Taichi (author) (この著者で検索)
National Institute for Materials Science Research Center for Structural Materials/Materials Evaluation Field/Structural Thermodynamics Group
ORCID SAMURAI ;
TANG Xin (author) (この著者で検索)
National Institute for Materials Science Research Center for Magnetic and Spintronic Materials/Green Magnetic Materials Group
ORCID SAMURAI ;
TOZMAN KARANIKOLAS Pelin (author) (この著者で検索)
National Institute for Materials Science Global Networking Division/International Center for Young Scientists
;
KULESH Nikita (author) (この著者で検索)
National Institute for Materials Science Research Center for Magnetic and Spintronic Materials/Green Magnetic Materials Group
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/Green Magnetic Materials 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 ;
HONO Kazuhiro (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI
キーワード
Machine learning, Hard magnetic materials, SmFe12-based alloys
刊行年月日
更新時刻
2024-10-17 08:30:37 +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
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
1-s2.0-S0169743925000085-main.pdf
MADGUI: Multi-Application Design Graphical User Interface for active learning assisted by Bayesian optimization
ジャーナル論文
著者
ORCID SAMURAI ; ORCID SAMURAI
キーワード
Graphical user interface, Statistical analysis, Machine learning, Bayesian optimization, Process optimization, User-friendly
刊行年月日
2025-01-21
更新時刻
2025-01-27 12:30:30 +0900
s41524-025-01828-7.pdf
Autonomous closed-loop exploration of composition-spread films for the anomalous Hall effect
ジャーナル論文
著者
ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI
キーワード
Machine learning, Autonomous, Combinatorial, Anomalous Hall effect
刊行年月日
2025-11-19
更新時刻
2025-12-25 17:32:06 +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
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