ファイル種別: application/pdf キーワード: machine learning
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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
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
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)
National Institute for Materials Science
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
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
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
materials-17-02866.pdf
Multi-Objective Optimization of Adhesive Joint Strength and Elastic Modulus of Adhesive Epoxy with Active Learning
ジャーナル論文
著者
ORCID SAMURAI ; ORCID SAMURAI ;
Chao Kang (author) (この著者で検索)
ORCID ;
Chiaki Sato (author) (この著者で検索)
キーワード
experimental testing, multi-objective optimization, epoxy, adhesive, elastic modulus, machine learning, active learning
刊行年月日
2024-06-12
更新時刻
2025-01-07 08:30:26 +0900
tstm_a_2691688_sm0812.pdf
An interpretable linear model bridging data-driven analysis and chemical intuition for Eu 2+ -phosphor emissions
ジャーナル論文
著者
ORCID SAMURAI ;
Ryusei Hayasaka (author) (この著者で検索)
;
Yuta Matsushima (author) (この著者で検索)
ORCID ; ORCID SAMURAI ; ORCID SAMURAI ;
Naoto Hirosaki (author) (この著者で検索)
キーワード
materials informatics, phosphor, europium, machine learning, interpretability, linear model, composition-based feature
刊行年月日
2026-12-31
更新時刻
2026-07-07 14:31:04 +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)
National Institute for Materials Science
ORCID SAMURAI ;
Masao Hayakawa (author) (この著者で検索)
National Institute for Materials Science
ORCID SAMURAI ;
Hiroyuki Masuda (author) (この著者で検索)
National Institute for Materials Science
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
Automatic_extraction_of_materials_and_properties_from_superconductors_scientific_literature-v1.pdf
Automatic Extraction of Materials and Properties from Superconductors Scientific Literature
ジャーナル論文
著者
Foppiano Luca (author) (この著者で検索)
NIMS MaDIS
ORCID ;
Baptista de Castro, Pedro (author) (この著者で検索)
NIMS MANA
ORCID ;
Ortiz Suarez, Pedro (author) (この著者で検索)
University of Mannheim Data and Web Science Group
ORCID ; ORCID SAMURAI ; ORCID SAMURAI ;
Ishii, Masashi (author) (この著者で検索)
NIMS MaDIS
ORCID
キーワード
tdm, machine learning, materials science, superconductors, database
刊行年月日
2022-09-15
更新時刻
2022-10-16 01:44:51 +0900
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)
National Institute for Materials Science
ORCID SAMURAI ;
Yutaka Iwasaki (author) (この著者で検索)
National Institute for Materials Science
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