キーワード: machine learning

42 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。

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

Pitfalls in Artificial Intelligence_revised_clean_v2.docx
Pitfalls in Artificial Intelligence Powered Discovery Due to Electrocatalyst Evaluation Methodologies
ジャーナル論文
著者
Abraham Castro Garcia (author) (この著者で検索)
;
Ken Sakaushi (author) (この著者で検索)
ORCID SAMURAI
キーワード
machine learning
刊行年月日
2025-09-04
更新時刻
2026-04-30 12:01:11 +0900

Gracheva_2026_Mach._Learn.__Sci._Technol._7_045006.pdf
Enhancing oxygen reduction reaction mass activity in polymer electrolyte fuel cells via molecular machine learning
ジャーナル論文
著者
ORCID SAMURAI ;
Shin-ichi Yamazaki (author) (この著者で検索)
; ORCID SAMURAI ; ORCID SAMURAI ;
Tsutomu Ioroi (author) (この著者で検索)
;
Masafumi Asahi (author) (この著者で検索)
ORCID
キーワード
materials science, machine learning, chemoinformatics, polymer electrolyte fuel cell (PEFC), oxygen reduction reaction, active learning
刊行年月日
2026-08-01
更新時刻
2026-08-26 16:18:22 +0900

esi_clean.pdf
Multiobjective Solid Electrolyte Design of Tetragonal and Cubic Inverse-Perovskites for All-Solid-State Lithium-Ion Batteries by High-Throughput Density Functional Theory Calculations and AI-Driven Methods
ジャーナル論文
著者
JALEM Randy (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/Interface Electrochemistry Group
ORCID SAMURAI ;
TATEYAMA Yoshitaka (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/Interface Electrochemistry Group
ORCID SAMURAI ;
TAKADA Kazunori (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/Solid-State Battery Group
ORCID SAMURAI ;
JANG Seonghoon (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/Interface Electrochemistry Group
キーワード
all solid state batteries, solid electrolytes, density functional theory, materials informatics, machine learning, novel materials search
刊行年月日
2023-09-07
更新時刻
2024-08-28 08:30:15 +0900

Dielectric tensor of perovskite oxides at finite temperature using equivariant graph neural network potentials.pdf
Dielectric tensor of perovskite oxides at finite temperature using equivariant graph neural network potentials
ジャーナル論文
著者
Alex Kutana (author) (この著者で検索)
Nagoya University
;
Koki Yoshimochi (author) (この著者で検索)
;
Ryoji Asahi (author) (この著者で検索)
キーワード
Graph neural network, machine learning, dielectrics, perovskite oxides, phase transitions
刊行年月日
2025-12-31
更新時刻
2025-07-18 10:24:15 +0900

jmca2024_NaSulfideConductor_esi_rj.pdf
Computational discovery of stable Na-ion sulfide solid electrolytes with high conductivity at room temperature
ジャーナル論文
著者
Seong-Hoon Jang (author) (この著者で検索)
ORCID ;
Randy Jalem (author) (この著者で検索)
ORCID SAMURAI ;
Yoshitaka Tateyama (author) (この著者で検索)
ORCID SAMURAI
キーワード
all solid state batteries, solid electrolytes, high-throughput first-principles calculation, computational materials search, machine learning, sodium ion conductors
刊行年月日
2024-08-05
更新時刻
2024-10-10 16:30:52 +0900

STAM.pdf
Essential structural and experimental descriptors for bulk and grain boundary conductivities of Li solid electrolytes
ジャーナル論文
著者
Yen-Ju Wu (author) (この著者で検索)
ORCID SAMURAI ;
Takehiro Tanaka (author) (この著者で検索)
;
Tomoyuki Komori (author) (この著者で検索)
;
Mikiya Fujii (author) (この著者で検索)
;
Hiroshi Mizuno (author) (この著者で検索)
;
Satoshi Itoh (author) (この著者で検索)
National Institute for Materials Science
ORCID ;
Tadanobu Takada (author) (この著者で検索)
;
Erina Fujita (author) (この著者で検索)
ORCID SAMURAI ;
Yibin Xu (author) (この著者で検索)
ORCID SAMURAI
キーワード
Ionic conductivity, machine learning, grain boundary, ionic conductor, Li battery, grain size, descriptor
刊行年月日
2020-01-31
更新時刻
2024-01-05 22:11:22 +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

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

STAMMethods4(2024)2326305.pdf
Alloys innovation through machine learning: a statistical literature review
ジャーナル論文
著者
Alireza Valizadeh (author) (この著者で検索)
;
Ryoji Sahara (author) (この著者で検索)
ORCID SAMURAI ;
Maaouia Souissi (author) (この著者で検索)
キーワード
Alloy development, machine learning, data-driven research, materials informatics, Materials Genome Initiative, Materials databases
刊行年月日
2024-12-31
更新時刻
2024-06-25 12:30:16 +0900

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