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machine learning
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Acquiring and transferring comprehensive catalyst knowledge through integrated high-throughput experimentation and automatic feature engineering
Description/Abstract:
Solid catalyst development has traditionally relied on trial-and-error approaches, limiting the broader application of valuable insights ...
Keyword:
Catalyst informatics
,
descriptor
,
high-throughput experimentation
,
machine learning
, and
oxidative coupling of methane
Resource Type:
Article
Author:
Ayu Fujiwara
,
Sunao Nakanowatari
,
Yohei Cho
, and
Toshiaki Taniike
Journal:
Science and Techonology of Advanced Materials
Date Uploaded:
23/01/2025
機械学習高速自動スペクトル解析ソフト“EMPeaks”について
Description/Abstract:
There are a wide variety of measurement techniques that produce spectra as output datasets. Existing data analysis software and some open...
Keyword:
data analysis
,
machine learning
,
peak fitting
, and
spectroscopy
Resource Type:
Article
Author:
永村 直佳
and
安藤 康伸
Journal:
表面と真空
Date Uploaded:
10/01/2025
Analysis of the effect of the Fermi surface matching at Co–Fe and Cu interface on giant magnetoresistance effect using a combinatorial technique
Description/Abstract:
Interfacial electronic band-matching (EBM) plays a crucial role in determining the spin-dependent transport properties and performance of...
Keyword:
Fermi surface
,
giant magnetoresistance
,
interfacial electronic band matching
,
machine learning
, and
spintronics
Resource Type:
Article
Author:
Varun Kumar Kushwaha
,
Ryo Toyama
,
Yoshio Miura
,
Yuma Iwasaki
, and
Yuya Sakuraba
Journal:
AIP Advances
Date Uploaded:
07/01/2025
Multi-Objective Optimization of Adhesive Joint Strength and Elastic Modulus of Adhesive Epoxy with Active Learning
Description/Abstract:
Studying multiple properties of a material concurrently is essential for obtaining a comprehensive understanding of its behavior and perf...
Keyword:
active learning
,
adhesive
,
elastic modulus
,
epoxy
,
experimental testing
,
machine learning
, and
multi-objective optimization
Resource Type:
Article
Author:
Paripat Kraisornkachit
,
Masanobu Naito
,
Chao Kang
, and
Chiaki Sato
Journal:
Materials
Date Uploaded:
06/01/2025
Estimating the S-N Curve by Machine Learning Random Forest Method
Description/Abstract:
Fatigue limit is well predicted by tensile strength or hardness, and the relationship is often analyzed by linear regression using the mi...
Keyword:
data-sheet
,
fatigue
,
high-cycle fatigue
,
machine learning
, and
random forest method
Resource Type:
Article
Author:
Nobuo Nagashima
,
Masao Hayakawa
,
Hiroyuki Masuda
, and
Kotobu Nagai
Journal:
MATERIALS TRANSACTIONS
Date Uploaded:
27/12/2024
Data-driven approaches for designing thermal insulating materials
Description/Abstract:
The design of high-performance thermal insulating materials is crucial for efficient thermal management in modern applications. This pres...
Keyword:
Interfacial thermal resistance
,
data-driven technique
,
machine learning
, and
thermal insulator
Resource Type:
Presentation
Author:
Yen-Ju Wu
and
Yibin Xu
Date Uploaded:
25/12/2024
Machine Learning as a “Catalyst” for Advancements in Carbon Nanotube Research
Description/Abstract:
The synthesis, characterization, and application of carbon nanotubes (CNTs) have long posed significant challenges due to the inherent mu...
Keyword:
carbon nanotube
,
growth mechanism
,
in situ TEM
, and
machine learning
Resource Type:
Article
Author:
Guohai Chen
and
Dai-Ming Tang
Journal:
Nanomaterials
Date Uploaded:
23/12/2024
Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns
Description/Abstract:
Since the discovery of the quasicrystal, approximately 100 stable quasicrystalsare identified. To date, the existence of quasicrystals is ...
Keyword:
deep learning
,
machine learning
,
phase identification
,
quasicrystal
, and
x-ray powder diffraction
Resource Type:
Article
Author:
Hirotaka Uryu
,
Tsunetomo Yamada
,
Koichi Kitahara
,
Alok Singh
,
Yutaka Iwasaki
,
Kaoru Kimura
,
Kanta Hiroki
,
Naoki Miyao
,
Asuka Ishikawa
,
Ryuji Tamura
,
Satoshi Ohhashi
,
Chang Liu
, and
Ryo Yoshida
Journal:
Advanced Science
Date Uploaded:
13/12/2024
Black-box optimization technique for investigation of surface phase diagram
Description/Abstract:
Surface phase diagrams are useful in material design for understanding catalytic reactions and deposition processes and are usually obtai...
Keyword:
machine learning
and
surface phase diagram
Resource Type:
Article
Author:
Makoto Urushihara
,
Kenji Yamaguchi
, and
Ryo Tamura
Journal:
AIP Advances
Date Uploaded:
10/12/2024
Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm
Description/Abstract:
Machine learning technology has a great potential to accelerate the discovery of innovative functional materials. We demonstrate a succes...
Keyword:
machine learning
,
molecular design
,
polymer
, and
thermal conductivity
Resource Type:
Article
Author:
Stephen Wu
,
Yukiko Kondo
,
Masa-aki Kakimoto
,
Bin Yang
,
Hironao Yamada
,
Isao Kuwajima
,
Guillaume Lambard
,
Kenta Hongo
,
Yibin Xu
,
Junichiro Shiomi
,
Christoph Schick
,
Junko Morikawa
, and
Ryo Yoshida
Journal:
npj Computational Materials
Date Uploaded:
21/11/2024
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