キーワード: Machine learning
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30 件のレコードが見つかりました。

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
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
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
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
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
Autonomous search for materials with high Curie temperature using ab initio calculations and machine learning (1).pdf
Autonomous search for materials with high Curie temperature using ab initio calculations and machine learning
ジャーナル論文
著者
ORCID SAMURAI
キーワード
Machine learning, Autonomous, Curie temperature, ab initio
刊行年月日
2024-12-31
更新時刻
2024-10-04 08:30:28 +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
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
Editorial_Nanomaterials.pdf
Advances in Carbon Nanotubes: Synthesis, Properties, and Cutting-Edge Applications
ジャーナル論文
著者
Guohai Chen (author) (この著者で検索)
ORCID ; ORCID SAMURAI
キーワード
Carbon Nanotubes, Synthesis, Properties, Machine learning
刊行年月日
2025-10-20
更新時刻
2025-12-13 08:30:03 +0900