キーワード: Machine learning

40 件のレコードが見つかりました。

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)
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)
ORCID SAMURAI ;
Takashi Nakanishi (author) (この著者で検索)
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

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)
ORCID SAMURAI ;
H. Sepehri-Amin (author) (この著者で検索)
ORCID SAMURAI ;
K. Hono (author) (この著者で検索)
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

1-s2.0-S2214860425001010-main.pdf
Phase-separation induced dislocation-network cellular structures in Ti-Zr-Nb-Mo-Ta high-entropy alloy processed by laser powder bed fusion
ジャーナル論文
著者
Han Chen (author) (この著者で検索)
;
Daisuke Egusa (author) (この著者で検索)
;
Zehao Li (author) (この著者で検索)
National Institute for Materials Science
;
Taisuke Sasaki (author) (この著者で検索)
ORCID SAMURAI ;
Ryosuke Ozasa (author) (この著者で検索)
;
Takuya Ishimoto (author) (この著者で検索)
;
Masayuki Okugawa (author) (この著者で検索)
;
Yuichiro Koizumi (author) (この著者で検索)
;
Takayoshi Nakano (author) (この著者で検索)
;
Eiji Abe (author) (この著者で検索)
キーワード
High-entropy alloy, Laser powder bed fusion, Cellular structure, Phase separation, Dislocation-network, Electron microscopy, Machine learning
刊行年月日
2025-03-12
更新時刻
2025-04-08 13:16:16 +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_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

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)
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)
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)
ORCID SAMURAI ;
Guillaume Lambard (author) (この著者で検索)
ORCID SAMURAI ;
Keitaro Sodeyama (author) (この著者で検索)
ORCID SAMURAI ;
Tetsushi Taguchi (author) (この著者で検索)
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

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