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
- ジャーナル論文
- 著者
-
Ryo Tamura (author) (この著者で検索)National Institute for Materials Science
;
;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
;
Takashi Nakanishi (author) (この著者で検索)National Institute for Materials Science
- 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
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
-
Lambard, Guillaume (author) (この著者で検索)
;
Sasaki, Taisuke (author) (この著者で検索)
;
Sodeyama, Keitaro (author) (この著者で検索)
;
Ohkubo, Tadakatsu (author) (この著者で検索)
;
Hono, Kazuhiro (author) (この著者で検索)
- 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
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
Toward the Atomic-Level Analysis of Ground-State Electronic Structures of Solid Materials via Prediction from Core-Loss Spectra: A Computational Study in Si
- ジャーナル論文
- 著者
-
;Izumi Takahara (author) (この著者で検索)Fumihiko Uesugi (author) (この著者で検索)National Institute for Materials Science
;
Koji Kimoto (author) (この著者で検索)National Institute for Materials Science
;
;Kiyou Shibata (author) (この著者で検索)Teruyasu Mizoguchi (author) (この著者で検索)
- キーワード
- Machine learning, ELNES, XANES, PDOS
- 刊行年月日
- 2024-08-15
- 更新時刻
- 2024-10-02 12:30:19 +0900
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
;
;Koji Shimizu (author) (この著者で検索)Satoshi Watanabe (author) (この著者で検索)
https://orcid.org/0000-0002-8069-6938
(unauthenticated)
National Institute for Materials Science
- 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
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)
https://orcid.org/0000-0001-8481-0972
(unauthenticated)
;
Dai-Ming Tang (author) (この著者で検索)
- 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
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
;
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
;
;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 GroupASHOK 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
;
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
;
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
;
;TOZMAN KARANIKOLAS Pelin (author) (この著者で検索)National Institute for Materials Science Global Networking Division/International Center for Young ScientistsKULESH 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
;
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
;
OHKUBO Tadakatsu (author) (この著者で検索)National Institute for Materials Science Research Center for Magnetic and Spintronic Materials
;
HONO Kazuhiro (author) (この著者で検索)National Institute for Materials Science
- 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
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 ScienceYuma Iwasaki (author) (この著者で検索)National Institute for Materials Science
- 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