資源タイプ: ジャーナル論文 キーワード: Machine learning
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24 件のレコードが見つかりました。 1件目から10件目まで表示します。

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1-s2.0-S135964542400692X-main.pdf
Process optimization on kesterite-based ceramics for enhancing their thermoelectric performances assisted by active machine learning approach: A tool for metal-sulfide ceramics development
ジャーナル論文
著者
Cédric Bourgès (author) (この著者で検索)
National Institute for Materials Science
ORCID ;
Guillaume Lambard (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Naoki Sato (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Makoto Tachibana (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Satoshi Ishii (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Takao Mori (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI
キーワード
Kesterite, Machine learning, Process, Thermoelectric, Ceramic
刊行年月日
2024-08-29
更新時刻
2024-09-20 16:30:30 +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
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)
National Institute for Materials Science
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)
National Institute for Materials Science
ORCID SAMURAI ;
Takashi Nakanishi (author) (この著者で検索)
National Institute for Materials Science
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
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)
National Institute for Materials Science
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
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
takahara-et-al-2024-toward-the-atomic-level-analysis-of-ground-state-electronic-structures-of-solid-materials-via.pdf
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
ORCID SAMURAI ;
Koji Kimoto (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
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
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
1-s2.0-S092702562600087X-main.pdf
Active learning for predicting the enthalpy of mixing in binary liquids based on ab initio molecular dynamics
ジャーナル論文
著者
Quentin Bizot (author) (この著者で検索)
ORCID ; ORCID SAMURAI ;
Guillaume Deffrennes (author) (この著者で検索)
ORCID
キーワード
CALPHAD, Machine learning
刊行年月日
2026-02-07
更新時刻
2026-04-01 13:44:57 +0900
lin-et-al-2025-determination-of-stable-proton-configurations-by-black-box-optimization-using-an-ising-machine.pdf
Determination of Stable Proton Configurations by Black-Box Optimization Using an Ising Machine
ジャーナル論文
著者
Jianbo Lin (author) (この著者で検索)
;
Tomofumi Tada (author) (この著者で検索)
ORCID ;
Ai Koizumi (author) (この著者で検索)
;
Masato Sumita (author) (この著者で検索)
ORCID ; ORCID SAMURAI ; ORCID SAMURAI
キーワード
Machine learning, Ising machine, Proton configuration
刊行年月日
2025-02-06
更新時刻
2025-05-23 08:30:18 +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)
National Institute for Materials Science
ORCID SAMURAI ;
H. Sepehri-Amin (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
K. Hono (author) (この著者で検索)
National Institute for Materials Science
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
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