ファイル種別: application/pdf キーワード: Machine learning
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24 件のレコードが見つかりました。

Minami_20250723final_w_highlight.pdf
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
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
Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis.pdf
Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis
ジャーナル論文
著者
Ryo Tamura (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Kenji Nagata (author) (この著者で検索)
;
Keitaro Sodeyama (author) (この著者で検索)
;
Kensaku Nakamura (author) (この著者で検索)
;
Toshiki Tokuhira (author) (この著者で検索)
;
Satoshi Shibata (author) (この著者で検索)
;
Kazuki Hammura (author) (この著者で検索)
;
Hiroki Sugisawa (author) (この著者で検索)
;
Masaya Kawamura (author) (この著者で検索)
;
Teruki Tsurimoto (author) (この著者で検索)
;
Masanobu Naito (author) (この著者で検索)
;
Masahiko Demura (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Takashi Nakanishi (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI
キーワード
Polypropylene, X-ray diffraction, Bayesian spectral deconvolution, Ising machine, Machine learning
刊行年月日
2024-12-31
更新時刻
2024-08-30 16:31:01 +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
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
Abstracts_Krishnan.pdf
From Random Networks to AI-Driven Glass Design
プレゼンテーション
著者
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
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
Improving efficiency of autonomous material search via transfer learning from nontarget properties (2).pdf
Improving efficiency of autonomous material search via transfer learning from nontarget properties
ジャーナル論文
著者
Jaekyun Hwang (author) (この著者で検索)
National Institute for Materials Science
;
Yuma Iwasaki (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI
キーワード
Machine learning
刊行年月日
2023-12-31
更新時刻
2024-01-05 22:11:31 +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
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