資源タイプ: ジャーナル論文 キーワード: machine learning
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3-12-Revised Manuscript without Any Highlighting.docx
Machine-Learning-Driven Discovery of Mn4+-Doped Red-Emitting Fluorides with Short Excited-State Lifetime and High Efficiency for Mini Light-Emitting Diode Displays
ジャーナル論文
著者
Hong Ming (author) (この著者で検索)
ORCID ;
Yayun Zhou (author) (この著者で検索)
;
Maxim S. Molokeev (author) (この著者で検索)
ORCID ;
Chuang Zhang (author) (この著者で検索)
;
Lin Huang (author) (この著者で検索)
;
Yuanjing Wang (author) (この著者で検索)
; ORCID SAMURAI ;
Enhai Song (author) (この著者で検索)
ORCID ;
Qinyuan Zhang (author) (この著者で検索)
ORCID
キーワード
luminescent materials, machine learning
刊行年月日
2024-05-06
更新時刻
2025-04-03 08:30:20 +0900
2025 ogawa APL final.pdf
Er-driven magnetic tunability in FePt thin films investigated via high-throughput experiments and microstructure analysis for future HAMR media
ジャーナル論文
著者
Daisuke Ogawa (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Yuma Iwasaki (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Jun Uzuhashi (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Yuta Sasaki (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Masato Kotsugi (author) (この著者で検索)
;
Yukiko K. Takahashi (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI
キーワード
heat-assisted magnetic recording, FePt, combinatorial, high throughput, machine learning, rare-earth doping
刊行年月日
2025-06-23
更新時刻
2025-07-11 08:30:48 +0900
Development of a method to evaluate strain in weld solidification using in-situ observations with high-brightness synchrotron X-rays.pdf
Development of a method to evaluate strain in weld solidification using in-situ observations with high-brightness synchrotron X-rays
ジャーナル論文
著者
ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI ;
Takayuki Yamashita (author) (この著者で検索)
;
Yasuhiro Aoki (author) (この著者で検索)
;
Hidetoshi Fujii (author) (この著者で検索)
キーワード
in-situ observation, synchrotron X-ray, welding technology, optical flow, machine learning, Strain evaluation method;
刊行年月日
2024-12-31
更新時刻
2024-11-13 12:30:30 +0900
Development of an AI-based acoustic disturbance-detection method for robotic arc welding processes.pdf
Development of an AI-based acoustic disturbance-detection method for robotic arc welding processes
ジャーナル論文
著者
Houichi Kitano (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Masaki Kobayashi (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Masahiko Demura (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI
キーワード
Acoustic sensing, in-process monitoring, gas metal arc welding, disturbance detection, machine learning, explainable AI, smart manufacturing
刊行年月日
2026-12-31
更新時刻
2026-06-25 09:51:40 +0900
Understanding strain localization in metallic materials a review of high-resolution digital image correlation and related techniques.pdf
Understanding strain localization in metallic materials: a review of high-resolution digital image correlation and related techniques
ジャーナル論文
著者
ORCID SAMURAI ;
T. E.J. Edwards (author) (この著者で検索)
;
J. Quinta da Fonseca (author) (この著者で検索)
;
J. -C. Stinville (author) (この著者で検索)
;
D. Texier (author) (この著者で検索)
;
T. Vermeij (author) (この著者で検索)
キーワード
High-resolution digital image correlation, strain localization, crystal plasticity, data merging, metallic materials, machine learning
刊行年月日
2026-12-31
更新時刻
2026-03-13 08:30:04 +0900
d5dd00486a.pdf
Bayesian diversity control for batch-based phase diagram determination
ジャーナル論文
著者
Peiheng Zou (author) (この著者で検索)
; ORCID SAMURAI ; ORCID SAMURAI
キーワード
phase diagram, machine learning
刊行年月日
2026-02-16
更新時刻
2026-04-01 14:33:21 +0900
Manuscript.pdf
Graph Network-Based Simulation of Multicellular Dynamics Driven by Concentrated Polymer Brush-Modified Cellulose Nanofibers
ジャーナル論文
著者
Chiaki Yoshikawa (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Duc Anh Nguyen (author) (この著者で検索)
;
Tadashi Nakaji-Hirabayashi (author) (この著者で検索)
;
Ichigaku Takigawa (author) (この著者で検索)
;
Hiroshi Mamitsuka (author) (この著者で検索)
キーワード
cellulose nanofiber, concentrated polymer brush, hMSC, self-assembly, machine learning
刊行年月日
2024-04-08
更新時刻
2024-08-27 08:30:31 +0900
d2ma00881e.pdf
Rapid discovery of new Eu2+-activated phosphors with a designed luminescence color using a data-driven approach
ジャーナル論文
著者
Yukinori Koyama (author) (この著者で検索)
National Institute for Materials Science Research and Services Division of Materials Data and Integrated System
ORCID SAMURAI ;
Hidekazu Ikeno (author) (この著者で検索)
Osaka Metropolitan University
;
Masamichi Harada (author) (この著者で検索)
National Institute for Materials Science Research Center for Functional Materials
;
Shiro Funahashi (author) (この著者で検索)
National Institute for Materials Science Research Center for Functional Materials
;
Takashi Takeda (author) (この著者で検索)
National Institute for Materials Science Research Center for Functional Materials
ORCID SAMURAI ;
Naoto Hirosaki (author) (この著者で検索)
National Institute for Materials Science Research Center for Functional Materials
キーワード
phosphor, materials design, machine learning, luminescence, emission spectrum, Eu2+
刊行年月日
2022-11-29
更新時刻
2024-01-05 22:11:23 +0900
125008_1_5.0229856.pdf
Black-box optimization technique for investigation of surface phase diagram
ジャーナル論文
著者
Makoto Urushihara (author) (この著者で検索)
;
Kenji Yamaguchi (author) (この著者で検索)
ORCID ; ORCID SAMURAI
キーワード
machine learning, surface phase diagram
刊行年月日
2024-12-01
更新時刻
2024-12-10 16:55:25 +0900
Manuscript_clean.pdf
Machine learning prediction of Young's modulus in multi component titanium based biomedical alloys using extended thermodynamic descriptors
ジャーナル論文
著者
Hassan Ahmad (author) (この著者で検索)
Pakistan Institute of Engineering and Applied Sciences (PIEAS) Department of Metallurgy and Materials Engineering
;
Muhammad Haider (author) (この著者で検索)
;
Zafar Iqbal (author) (この著者で検索)
;
Muhammad Zarif (author) (この著者で検索)
;
Syed Mujtaba Ul Hassan (author) (この著者で検索)
キーワード
Titanium alloys, machine learning, biomedical alloys, Young’s modulus prediction
刊行年月日
2026-12-31
更新時刻
2026-07-08 16:58:38 +0900
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