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Koji Tsuda
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1.
Revealing factors influencing polymer degradation with rank-based machine learning
Description/Abstract:
The efficient treatment of polymer waste is a major challenge to marine sustainability. It is useful to reveal the factors that dominate ...
Keyword:
PoLyInfo
,
polymer degradation
, and
rank-based machine learning
Resource Type:
Article
Author:
Weilin Yuan
,
Yusuke Hibi
,
Ryo Tamura
,
Masato Sumita
,
Yasuyuki Nakamura
,
Masanobu Naito
, and
Koji Tsuda
Journal:
Patterns
Date Uploaded:
10/11/2023
2.
NIMS-OS: An automation software to implement a closed loop between artificial intelligence and robotic experiments in materials science
Description/Abstract:
NIMS-OS (NIMS Orchestration System) is a Python library created to realize a closed loop of robotic experiments and artificial intelligen...
Keyword:
NIMS-OS
,
artificial intelligence
, and
robotic experiments
Resource Type:
Article
Author:
Ryo Tamura
,
Koji Tsuda
, and
Shoichi Matsuda
Journal:
Science and Technology of Advanced Materials: Methods
Date Uploaded:
20/10/2023
3.
Ranking Pareto optimal solutions based on projection free energy
Description/Abstract:
Based on available datasets prepared by numerical simulations and machine learning, maps of properties for materials that have not yet be...
Keyword:
Pareto solutions
,
free energy
, and
semiconductor
Resource Type:
Article
Author:
Ryo Tamura
,
Kei Terayama
,
Masato Sumita
, and
Koji Tsuda
Journal:
Physical Review Materials
Date Uploaded:
04/10/2023
Date Modified:
04/10/2023
4.
Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring
Description/Abstract:
AIを用いた機能性有機化合物の開発。
Keyword:
De novo molecule generation
,
characteristic functional group monitoring
,
chromophore
, and
deep learning
Resource Type:
Article
Author:
Takehiro Fujita
,
Kei Terayama
,
Masato Sumita
,
Ryo Tamura
,
Yasuyuki Nakamura
,
Masanobu Naito
, and
Koji Tsuda
Date Uploaded:
15/02/2023
Date Modified:
20/03/2024
5.
CrySPY: a crystal structure prediction tool accelerated by machine learning
Description/Abstract:
We have developed an open-source software called CrySPY, which is a crystal structure prediction tool written in Python 3, ...
Keyword:
evolutionary algorithm
,
Bayesian optimization
,
LAQA
,
crystal structure prediction
, and
first-principles calculations
Resource Type:
Article
Author:
Tomoki Yamashita
,
Shinichi Kanehira
,
Nobuya Sato
,
Hiori Kino
,
Kei Terayama
,
Hikaru Sawahata
,
Takumi Sato
,
Futoshi Utsuno
,
Koji Tsuda
,
Takashi Miyake
, and
Tamio Oguchi
Date Uploaded:
10/02/2023
Date Modified:
02/04/2024
6.
Monte Carlo tree search for materials design and discovery
Description/Abstract:
Materials design and discovery can be represented as selecting the optimal structure from a space of candidates that optimizes a target p...
Keyword:
Materials design and discovery
Resource Type:
Article
Author:
Ju, Shenghong
,
Tsuda, Koji
,
Dieb, Thaer M.
, and
Shiomi, Junichiro
Journal:
MRS Communications
Date Uploaded:
02/10/2020
Date Modified:
16/10/2020
7.
Structure prediction of boron-doped graphene by machine learning
Description/Abstract:
Heteroatom doping has endowed graphene with manifold aspects of material properties and boosted its applications. The atomic structure de...
Keyword:
Materials design
and
Monte Carlo tree search
Resource Type:
Article
Author:
Hou, Zhufeng
,
Tsuda, Koji
, and
Dieb, Thaer M.
Journal:
The Journal of Chemical Physics
Date Uploaded:
02/10/2020
Date Modified:
16/10/2020
8.
MDTS: automatic complex materials design using Monte Carlo tree search
Description/Abstract:
Complex materials design is often represented as a black-box combinatorial optimization problem. In this paper, we present a novel python...
Keyword:
Monte Carlo tree search
and
materials design
Resource Type:
Article
Author:
Ju, Shenghong
,
Tsuda, Koji
,
Shiomi, Junichiro
,
Yoshizoe, Kazuki
,
Dieb, Thaer M.
, and
Hou, Zhufeng
Journal:
Science and Technology of Advanced Materials
Date Uploaded:
02/10/2020
Date Modified:
16/10/2020
9.
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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9
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Materials design
2
Monte Carlo tree search
2
evolutionary algorithm
1
Bayesian optimization
1
De novo molecule generation
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Language
English
9
Publisher
National Institute for Materials Science
2
AIP Publishing
1
American Physical Society (APS)
1
Cambridge University Press
1
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1
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8
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9
Rights Statement Sim
Creative Commons BY Attribution 4.0 International
7
CC-BY-4.0
1
In Copyright
1
Date
2018
2
2017
1
2019
1
Author
Koji Tsuda
5
Dieb, Thaer M.
4
Ryo Tamura
4
Tsuda, Koji
4
Kei Terayama
3
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License
https://creativecommons.org/licenses/by/4.0/
4
Funder
JSPS KAKENHI
1
JST
1
MEXT
1
Support Program for Starting Up Innovation Hub
1
Journal
MRS Communications
1
Nanoinformatics
1
Patterns
1
Physical Review Materials
1
Science and Technology of Advanced Materials
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