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Machine learning
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Automated odor-blending with one-pot Bayesian optimization
Description/Abstract:
The creation of new odors by blending existing ones is usually done manually based on the human sense. To enable robots to perform this a...
Keyword:
Bayesian optimization
,
MSS
,
Machine learning
, and
Olfactory sensor
Resource Type:
Article
Author:
Yota Fukui
,
Kosuke Minami
,
Kota Shiba
,
Genki Yoshikawa
,
Koji Tsuda
, and
Ryo Tamura
Journal:
Digital Discovery
Date Uploaded:
10/06/2024
Improving efficiency of autonomous material search via transfer learning from nontarget properties
Description/Abstract:
Recently, autonomous material search methods combining machine learning and experiments/simulations have become indispensable for explori...
Keyword:
Machine learning
Resource Type:
Article
Author:
Jaekyun Hwang
and
Yuma Iwasaki
Journal:
Science and Technology of Advanced Materials: Methods
Date Uploaded:
27/10/2023
Efficient autonomous material search method combining ab initio calculations, autoencoder, and multi-objective Bayesian optimization
Description/Abstract:
Autonomous material search systems that combine ab initio calculations and Bayesian optimization are very promising for exploring huge ma...
Keyword:
Machine learning
,
ab-initio
, and
autonomous materials search
Resource Type:
Article
Author:
Yuma Iwasaki
,
Hwang Jaekyun
,
Yuya Sakuraba
,
Masato Kotsugi
, and
Yasuhiko Igarashi
Date Uploaded:
07/02/2023
Date Modified:
21/03/2024
Process parameters and magnetic properties (coercivity, remanence, squareness, maximum energy product) of data-driven fabrication of Nd-Fe-B anisotropic magnets by direct hot extrusion.
Description/Abstract:
We implemented an active learning pipeline assisted by machine learning and Bayesian optimization (ALMLBO) for predicting magnetic proper...
Keyword:
Active learning
,
Machine learning
,
Bayesian Optimization
,
Nd-Fe-B magnet
,
Hot extrusion
,
Process
,
Coercivity
,
Remanence
,
Squareness
, and
Maximum energy product
Material/Specimen:
Anisotropic permanent magnets fabricated by hot extrusion using a commercial Nd14Fe76Co3.4B6Ga0.6 (at%) powder (MQU-F™)
Resource Type:
Dataset
Data origin:
experiments
Author:
Lambard, Guillaume
,
Sasaki, Taisuke
,
Sodeyama, Keitaro
,
Ohkubo, Tadakatsu
, and
Hono, Kazuhiro
Operator:
PRYTULIAK, Anastasiia
and
TOYOOKA, Yoshiya
Date Uploaded:
11/05/2021
Date Modified:
11/10/2021
Prediction and optimization of epoxy adhesive strength from a small dataset through active learning
Description/Abstract:
Machine learning is emerging as a powerful tool for the discovery of novel high-performance functional materials. However, experimental d...
Keyword:
Machine learning
,
active learning
, and
adhesive
Resource Type:
Article
Author:
Pruksawan, Sirawit
,
Lambard, Guillaume
,
Samitsu, Sadaki
,
Sodeyama, Keitaro
, and
Naito, Masanobu
Journal:
Science and Technology of Advanced Materials
Date Uploaded:
02/01/2021
Date Modified:
18/10/2022
Machine Learning-Based Experimental Design in Materials Science
Description/Abstract:
In materials design and discovery processes, optimal experimental design (OED) algorithms are getting more popular. OED is often modeled ...
Keyword:
Machine learning
,
Materials design
, and
Optimal experiment design
Resource Type:
Part of Book
Author:
Tsuda, Koji
and
Dieb, Thaer M.
Journal:
Nanoinformatics
Date Uploaded:
02/10/2020
Date Modified:
16/10/2020
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5
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Keyword
Machine learning
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6
Active learning
1
Bayesian Optimization
1
Bayesian optimization
1
Coercivity
1
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Language
English
6
Publisher
National Institute for Materials Science
2
Informa UK Limited
1
Royal Society of Chemistry (RSC)
1
Springer, Singapore
1
Taylor & Francis
1
Resource type
Article
4
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1
Part of Book
1
Visibility
open
6
Rights Statement Sim
Creative Commons BY Attribution 4.0 International
4
CC-BY-4.0
1
MIT License
1
Computational methods
machine learning
1
Data origin
experiments
1
Properties addressed
magnetic -- coercivity
1
Synthesis and processing
forming -- extrusion
1
Characterization methods
other
1
Material/Specimen
Anisotropic permanent magnets fabricated by hot extrusion using a commercial Nd14Fe76Co3.4B6Ga0.6 (at%) powder (MQU-F™)
1
Date
2018
1
2019
1
Author
Lambard, Guillaume
2
Sodeyama, Keitaro
2
Yuma Iwasaki
2
Dieb, Thaer M.
1
Genki Yoshikawa
1
more
Authors
»
Operator
PRYTULIAK, Anastasiia
1
TOYOOKA, Yoshiya
1
License
https://creativecommons.org/licenses/by/4.0/
2
Instrument manufacturer
Tamakawa Co., Ltd
1
Zeiss
1
Instrument model number
Crossbeam 1540 EsB FIB/SEM
1
TM-BH25-C1
1
Funder
Japan Society for the Promotion of Science
1
Journal
Digital Discovery
1
Nanoinformatics
1
Science and Technology of Advanced Materials
1
Science and Technology of Advanced Materials: Methods
1