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deep learning
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Text‐to‐Microstructure Generation Using Generative Deep Learning
Description/Abstract:
Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modeling methods of mater...
Keyword:
deep learning
,
microstructure generation
, and
natural language processing
Resource Type:
Article
Author:
Xiaoyang Zheng
,
Ikumu Watanabe
,
Jamie Paik
,
Jingjing Li
,
Xiaofeng Guo
, and
Masanobu Naito
Journal:
Small
Date Uploaded:
19/09/2024
Estimation of Texture-Dependent Stress-Strain Curve and <i>r</i>-Value of Aluminum Alloy Sheet Using Deep Learning
Description/Abstract:
Deformation of an aluminum alloy sheet which is affected by its underlying crystallographic texture has been widely studied using crystal...
Keyword:
crystallite orientation
,
deep learning
,
flow stress
,
numerical analysis
,
tension test
, and
texture
Resource Type:
Article
Author:
Kohta Koenuma
,
Akinori Yamanaka
,
Ikumu Watanabe
, and
Toshihiko Kuwabara
Date Uploaded:
02/03/2023
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
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deep learning
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English
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Japan Institute of Metals
1
National Institute for Materials Science
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Wiley
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3
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CC-BY-4.0
1
Creative Commons BY Attribution 4.0 International
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Ikumu Watanabe
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Masanobu Naito
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Japan Society for the Promotion of Science
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Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
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Small
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