ファイル種別: application/pdf キーワード: machine learning
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25 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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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
74_Roadmap_J Phys Energy 2023_Roadmap on molecular modelling of electrochemical energy materials.pdf
2023 Roadmap on molecular modelling of electrochemical energy materials
ジャーナル論文
著者
Chao Zhang (author) (この著者で検索)
;
Jun Cheng (author) (この著者で検索)
;
Yiming Chen (author) (この著者で検索)
;
Maria K Y Chan (author) (この著者で検索)
;
Qiong Cai (author) (この著者で検索)
;
Rodrigo P Carvalho (author) (この著者で検索)
;
Cleber F N Marchiori (author) (この著者で検索)
;
Daniel Brandell (author) (この著者で検索)
;
C Moyses Araujo (author) (この著者で検索)
;
Ming Chen (author) (この著者で検索)
;
Xiangyu Ji (author) (この著者で検索)
;
Guang Feng (author) (この著者で検索)
;
Kateryna Goloviznina (author) (この著者で検索)
;
Alessandra Serva (author) (この著者で検索)
;
Mathieu Salanne (author) (この著者で検索)
;
Toshihiko Mandai (author) (この著者で検索)
National Institute for Materials Science (NIMS)
ORCID SAMURAI ;
Tomooki Hosaka (author) (この著者で検索)
;
Mirna Alhanash (author) (この著者で検索)
;
Patrik Johansson (author) (この著者で検索)
;
Yun-Ze Qiu (author) (この著者で検索)
;
Hai Xiao (author) (この著者で検索)
;
Michael Eikerling (author) (この著者で検索)
;
Ryosuke Jinnouchi (author) (この著者で検索)
;
Marko M Melander (author) (この著者で検索)
;
Georg Kastlunger (author) (この著者で検索)
;
Assil Bouzid (author) (この著者で検索)
;
Alfredo Pasquarello (author) (この著者で検索)
;
Seung-Jae Shin (author) (この著者で検索)
;
Minho M Kim (author) (この著者で検索)
;
Hyungjun Kim (author) (この著者で検索)
;
Kathleen Schwarz (author) (この著者で検索)
;
Ravishankar Sundararaman (author) (この著者で検索)
キーワード
electrochemical interfaces, density-functional theory, molecular dynamics simulation, electrochemical energy storage, machine learning, electrocatalysis
刊行年月日
2023-10-01
更新時刻
2024-03-22 00:53:39 +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)
National Institute for Materials Science
ORCID SAMURAI ;
Yoshitaka Tateyama (author) (この著者で検索)
National Institute for Materials Science
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
main.pdf
Automatic Identification and Normalisation of Physical Measurements in Scientific Literature
ジャーナル論文
著者
ORCID ;
ROMARY, Laurent (author) (この著者で検索)
ORCID ; ORCID SAMURAI ; ORCID
キーワード
tdm, physical quantities, machine learning
刊行年月日
2019-09-23
更新時刻
2022-10-03 02:00:14 +0900
AdvMater.25_37_e10239.pdf
Artificial Intelligence‐Driven Nanoarchitectonics for Smart Targeted Drug Delivery
ジャーナル論文
著者
Hayeon Bae (author) (この著者で検索)
;
Hyunsub Ji (author) (この著者で検索)
;
Konstantin Konstantinov (author) (この著者で検索)
;
Ronald Sluyter (author) (この著者で検索)
;
Katsuhiko Ariga (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Yong Ho Kim (author) (この著者で検索)
;
Jung Ho Kim (author) (この著者で検索)
キーワード
artificial intelligence, in silico optimization, machine learning, nanocarrier design, nanomedicine, targeted drug delivery
刊行年月日
2025-08-07
更新時刻
2025-10-28 12:30:13 +0900
manuscript.docx
Accelerating Materials Discovery of Novel Europium(II)-Activated Phosphors through Machine Learning Classification of Europium Valences
ジャーナル論文
著者
ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI ; ORCID SAMURAI
キーワード
phosphor, machine learning, europium, oxidation state
刊行年月日
2024-12-10
更新時刻
2025-11-25 08:30:03 +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
shrine20230330-1595-1im5f61.pdf
Unveiling the principle descriptor for predicting the electron inelastic mean free path based on a machine learning framework
ジャーナル論文
著者
Xun Liu (author) (この著者で検索)
National Institute for Materials Science
;
Zhufeng Hou (author) (この著者で検索)
;
Dabao Lu (author) (この著者で検索)
;
Bo Da (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Hideki Yoshikawa (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Shigeo Tanuma (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Yang Sun (author) (この著者で検索)
;
Zejun Ding (author) (この著者で検索)
キーワード
machine learning, inelastic mean free path, LASSO
刊行年月日
2019-12-31
更新時刻
2024-09-05 16:30:30 +0900
20241223Nagamura_著者最終稿.pdf
機械学習高速自動スペクトル解析ソフト“EMPeaks”について
ジャーナル論文
著者
永村 直佳 (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
安藤 康伸 (author) (この著者で検索)
キーワード
spectroscopy, peak fitting, data analysis, machine learning
刊行年月日
2024-10-10
更新時刻
2025-01-10 16:31:04 +0900
ジャーナル論文
A design methodology of crystal growth furnace and process aided by two-step optimization using machine learning models and genetic algorithm
ジャーナル論文
著者
Hiroyuki Tanaka (author) (この著者で検索)
Nagoya University a Graduate School of Engineering
;
Kentaro Kutsukake (author) (この著者で検索)
;
Kota Asakura (author) (この著者で検索)
;
Takuto Kojima (author) (この著者で検索)
;
Xin Liu (author) (この著者で検索)
;
Noritaka Usami (author) (この著者で検索)
キーワード
Optimization, machine learning, genetic algorithm, crystal growth, process informatics
刊行年月日
2025-12-31
更新時刻
2026-02-27 12:30:07 +0900
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