キーワード: Machine learning
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9 件のレコードが見つかりました。 1件目から9件目まで表示します。

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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
Efficient autonomous material search method combining ab initio calculations autoencoder and multi objective Bayesian optimization.pdf
Efficient autonomous material search method combining ab initio calculations, autoencoder, and multi-objective Bayesian optimization
ジャーナル論文
著者
ORCID SAMURAI ;
Hwang Jaekyun (author) (この著者で検索)
ORCID ; ORCID SAMURAI ;
Masato Kotsugi (author) (この著者で検索)
ORCID ;
Yasuhiko Igarashi (author) (この著者で検索)
ORCID
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
s41524-025-01757-5 (2).pdf
High-throughput materials exploration system for the anomalous Hall effect using combinatorial experiments and machine learning
ジャーナル論文
著者
ORCID SAMURAI ; ORCID SAMURAI ; ORCID ; ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI
キーワード
Machine learning, High-throughput, Combinatorial, Anomalous Hall effect
刊行年月日
2025-09-03
更新時刻
2025-12-26 13:11:05 +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
Autonomous search for half-metallic materials with B2 structure.pdf
Autonomous search for half-metallic materials with B 2 structure
ジャーナル論文
著者
ORCID SAMURAI ; ORCID SAMURAI ;
Takahiro Yamazaki (author) (この著者で検索)
ORCID ;
Yasuhiko Igarashi (author) (この著者で検索)
ORCID ;
Masato Kotsugi (author) (この著者で検索)
ORCID ; ORCID SAMURAI
キーワード
Machine learning, Autonomous, ab initio, Half metal
刊行年月日
2024-12-31
更新時刻
2024-10-04 08:30:32 +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
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
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
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
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