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Keitaro Sodeyama
Remove constraint Creator: Keitaro Sodeyama
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First-principles study of the reconstruction of MgM2O4 (M = Mn, Fe, Co) spinel surface
Description/Abstract:
MgM2O4 (M = Mn, Fe, Co) spinels, which transform into rock-salt phases on Mg incorporation, are attractive cathode materials for future M...
Keyword:
Cathode
,
First-principles calculations
,
Mg-battery
,
Spinel Surface
, and
Surface reconstruction
Resource Type:
Article
Author:
Tomoaki Kaneko
,
Yui Fujihara
,
Hiroaki Kobayashi
, and
Keitaro Sodeyama
Date Uploaded:
01/02/2023
Date Modified:
20/03/2024
Machine learning prediction of coordination energies for alkali group elements in battery electrolyte solvents
Description/Abstract:
We combined a data science-driven method with quantum chemistry calculations, and applied it to the battery electrolyte problem. We perfo...
Keyword:
electrolyte
,
battery
, and
quantum chemistry
Resource Type:
Dataset
Data origin:
simulations
Author:
ISHIKAWA, Atsushi
,
SODEYAMA, Keitaro
,
IGARASHI, Yasuhiko
,
NAKAYAMA, Tomofumi
,
TATEYAMA, Yoshitaka
, and
OKADA, Masato
Journal:
Physical Chemistry Chemical Physics
Date Uploaded:
24/09/2021
Date Modified:
01/10/2021
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
Liquid electrolyte informatics using an exhaustive search with linear regression
Description/Abstract:
Exploring new liquid electrolyte materials is a fundamental target for developing new high-performance lithium-ion batteries. In contrast...
Keyword:
Gaussian09
,
Li-ion battery
,
materials informatics
,
molecules
,
organic solvents
, and
quantum chemistry calculations
Material/Specimen:
organic solvents
Resource Type:
Dataset
Data origin:
simulations
Author:
SODEYAMA, Keitaro
,
IGARASHI, Yasuhiko
,
NAKAYAMA, Tomofumi
,
TATEYAMA, Yoshitaka
, and
OKADA, Masato
Journal:
Physical Chemistry Chemical Physics
Date Uploaded:
09/09/2020
Date Modified:
30/06/2021
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2
Keyword
Machine learning
2
Active learning
1
Bayesian Optimization
1
Cathode
1
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Language
English
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5
Publisher
Royal Society of Chemistry
2
Elsevier
1
National Institute for Materials Science
1
Taylor & Francis
1
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Dataset
3
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2
Visibility
open
5
Rights Statement Sim
Creative Commons BY Attribution 4.0 International
3
Creative Commons BY-NC Attribution-NonCommercial 4.0 International
1
MIT License
1
Computational methods
density functional theory or electronic structure
1
machine learning
1
Data origin
simulations
2
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
organic solvents
1
Date
2019
1
Author
IGARASHI, Yasuhiko
2
Lambard, Guillaume
2
NAKAYAMA, Tomofumi
2
OKADA, Masato
2
SODEYAMA, Keitaro
2
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
Journal
Physical Chemistry Chemical Physics
2
Science and Technology of Advanced Materials
1