キーワード: machine learning

37 件のレコードが見つかりました。

Advanced Science - 2023 - Uryu - Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder.pdf
Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns
ジャーナル論文
著者
Hirotaka Uryu (author) (この著者で検索)
;
Tsunetomo Yamada (author) (この著者で検索)
;
Koichi Kitahara (author) (この著者で検索)
;
Alok Singh (author) (この著者で検索)
ORCID SAMURAI ;
Yutaka Iwasaki (author) (この著者で検索)
ORCID SAMURAI ;
Kaoru Kimura (author) (この著者で検索)
National Institute for Materials Science
ORCID ;
Kanta Hiroki (author) (この著者で検索)
;
Naoki Miyao (author) (この著者で検索)
;
Asuka Ishikawa (author) (この著者で検索)
;
Ryuji Tamura (author) (この著者で検索)
;
Satoshi Ohhashi (author) (この著者で検索)
;
Chang Liu (author) (この著者で検索)
;
Ryo Yoshida (author) (この著者で検索)
キーワード
deep learning, x-ray powder diffraction, quasicrystal, phase identification, machine learning
刊行年月日
2023-11-14
更新時刻
2024-12-13 12:30:39 +0900

s41524-025-01851-8 (1).pdf
Networking autonomous material exploration systems through transfer learning
ジャーナル論文
著者
Naoki Yoshida (author) (この著者で検索)
;
Yutaro Iwabuchi (author) (この著者で検索)
;
Yasuhiko Igarashi (author) (この著者で検索)
;
Yuma Iwasaki (author) (この著者で検索)
ORCID SAMURAI
キーワード
machine learning
刊行年月日
2025-12-09
更新時刻
2025-12-13 08:30:26 +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)
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

Predicting the surface roughness of an electrodeposited copper film using a machine learning technique.pdf
Predicting the surface roughness of an electrodeposited copper film using a machine learning technique
ジャーナル論文
著者
Ryo Tamura (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 Algorithm Team
ORCID SAMURAI ;
Ryuichi Inaba (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Mami Watanabe (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Yutaro Mori (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Makoto Urushihara (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Kenji Yamaguchi (author) (この著者で検索)
MITSUBISHI MATERIALS CORPORATION
;
Shoichi Matsuda (author) (この著者で検索)
National Institute for Materials Science Research Center for Energy and Environmental Materials (GREEN)/Battery and Cell Materials Field/Automated Electrochemical Experiments Team
ORCID SAMURAI
キーワード
electrodeposited copper film, surface roughness, machine learning
刊行年月日
2024-12-31
更新時刻
2024-10-31 16:30:15 +0900

ChemPlusChem25_90_e202500342.pdf
Machine Learning—Guided Design of Biomass‐Based Porous Carbon for Aqueous Symmetric Supercapacitors
ジャーナル論文
著者
Manickam Minakshi (author) (この著者で検索)
ORCID ;
Apsana Sharma (author) (この著者で検索)
;
Ferdous Sohel (author) (この著者で検索)
;
Almantas Pivrikas (author) (この著者で検索)
ORCID ;
Pragati A. Shinde (author) (この著者で検索)
ORCID ; ORCID SAMURAI ; ORCID SAMURAI
キーワード
biomass, carbon, dopant, energy, machine learning, storage
刊行年月日
2025-10-09
更新時刻
2025-10-21 16:06:10 +0900

41524_2019_203_MOESM1_ESM.avi
Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm
ジャーナル論文
著者
Stephen Wu (author) (この著者で検索)
;
Yukiko Kondo (author) (この著者で検索)
;
Masa-aki Kakimoto (author) (この著者で検索)
;
Bin Yang (author) (この著者で検索)
;
Hironao Yamada (author) (この著者で検索)
; ORCID SAMURAI ;
Guillaume Lambard (author) (この著者で検索)
ORCID SAMURAI ;
Kenta Hongo (author) (この著者で検索)
; ORCID SAMURAI ;
Junichiro Shiomi (author) (この著者で検索)
;
Christoph Schick (author) (この著者で検索)
;
Junko Morikawa (author) (この著者で検索)
;
Ryo Yoshida (author) (この著者で検索)
キーワード
machine learning, polymer, thermal conductivity, molecular design
刊行年月日
2019-06-21
更新時刻
2024-11-21 16:31:23 +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)
ORCID SAMURAI ;
Yuma Iwasaki (author) (この著者で検索)
ORCID SAMURAI ;
Jun Uzuhashi (author) (この著者で検索)
ORCID SAMURAI ;
Yuta Sasaki (author) (この著者で検索)
ORCID SAMURAI ;
Masato Kotsugi (author) (この著者で検索)
;
Yukiko K. Takahashi (author) (この著者で検索)
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

2025秋季応用物理学会.pdf
Structure Prediction from Chemical Formula Using Periodic Descriptors
プレゼンテーション
著者
Yen-Ju Wu (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 Inorganic Materials Group
ORCID SAMURAI ;
Yibin Xu (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 Inorganic Materials Group
ORCID SAMURAI
キーワード
periodic descriptor, structure prediction, machine learning, template substitution
刊行年月日
更新時刻
2025-09-13 08:30:19 +0900

Estimation the S-N Curve by Machine Learning Random Forest Method_Mater. Trans. 65(2024)428-433.pdf
Estimating the S-N Curve by Machine Learning Random Forest Method
ジャーナル論文
著者
Nobuo Nagashima (author) (この著者で検索)
ORCID SAMURAI ;
Masao Hayakawa (author) (この著者で検索)
ORCID SAMURAI ;
Hiroyuki Masuda (author) (この著者で検索)
ORCID SAMURAI ;
Kotobu Nagai (author) (この著者で検索)
National Institute for Materials Science
キーワード
fatigue, high-cycle fatigue, data-sheet, machine learning, random forest method
刊行年月日
2024-04-01
更新時刻
2024-12-27 16:30:58 +0900

jpsj.94.031005.pdf
Self-Energy Spectroscopy and Artificial Neural Network
ジャーナル論文
著者
ORCID SAMURAI
キーワード
machine learning, quantum materials, photoemission spectroscopy, high-temperature superconductivity
刊行年月日
2025-03-15
更新時刻
2025-02-27 12:30:42 +0900