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。

Revised Manuscript-ActaMater_20240327.docx
ALMLBO PIPELINE APPROACH APPLY TO MATERIAL PROCESS AND COMPOSITION OPTIMIZATION FOR ENERGY-SAVING APPLICATIONS
プレゼンテーション
著者
Cédric Bourgès (author) (この著者で検索)
National Institute for Materials Science International Center for Young Scientists
ORCID ;
Guillaume Lambard (author) (この著者で検索)
National Institute for Materials Science Center for Basic Research on Materials/Data-driven Materials Research Field/Data-driven Materials Design Group
ORCID SAMURAI ;
Naoki Sato (author) (この著者で検索)
National Institute for Materials Science Research Center for Materials Nanoarchitectonics (MANA)/Nanomaterials Field/Thermal Energy Materials Group
ORCID SAMURAI ;
Makoto Tachibana (author) (この著者で検索)
National Institute for Materials Science Research Center for Materials Nanoarchitectonics (MANA)/Nanomaterials Field/Thermal Energy Materials Group
ORCID SAMURAI ;
Satoshi Ishii (author) (この著者で検索)
National Institute for Materials Science Research Center for Materials Nanoarchitectonics (MANA)/Nanomaterials Field/Optical Nanostructure Team
ORCID SAMURAI ;
Takao Mori (author) (この著者で検索)
National Institute for Materials Science Research Center for Materials Nanoarchitectonics (MANA)/Nanomaterials Field/Thermal Energy Materials Group
ORCID SAMURAI
キーワード
Kesterite, Machine learning, Process, Thermoelectric, Ceramic
刊行年月日
更新時刻
2024-10-30 16:30:23 +0900
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
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
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
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
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
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
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
DataSummary_Extrusion_Manuscript_20210728.xlsx
Process parameters and magnetic properties (coercivity, remanence, squareness, maximum energy product) of data-driven fabrication of Nd-Fe-B anisotropic magnets by direct hot extrusion.
データセット
著者
ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI
キーワード
Maximum energy product, Coercivity, Squareness, Machine learning, Remanence, Process, Bayesian Optimization, Hot extrusion, Active learning, Nd-Fe-B magnet
刊行年月日
2021-10-11
更新時刻
2022-10-03 02:01:08 +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