| Project/Area Number |
22KJ0407
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| Project/Area Number (Other) |
22J11202 (2022)
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund (2023)
Single-year Grants (2022) |
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| Section | 国内 |
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| Review Section |
Basic Section 26040:Structural materials and functional materials-related
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| Research Institution | National Institute for Materials Science (2023)
University of Tsukuba (2022) |
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Principal Investigator |
ZHENG Xiaoyang 国立研究開発法人物質・材料研究機構, マテリアル基盤研究センター, 特別研究員(PD)
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| Project Period (FY) |
2023-03-08 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2023: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2022: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | Material design / Mechanical metamaterial / Material informatics / Deep learning / Architected material / Materials and design / Finite element method / 3D printing / Inverse design |
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| Outline of Research at the Start |
This research will accelerate the revolution of designing complicated architected materials by removing guesswork from material design in a variety of applications. This work is based on deep learning with big data. A deep neural network will be trained using tens of thousands of structured data, which is similar to the way how species are differentiated and evolve by trial and error. The well-trained neural network is finally capable of generating flexible, tough auxetic metamaterials with extreme properties.
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| Outline of Annual Research Achievements |
In this year, I have three main works, including a review article in terms of deep learning in mechanical metamaterials, a research article in terms of multiphase metamaterials with highly variable stiffness, and a research article in terms of text-to-microstructure generation using deep learning.
In the review article, I provide a comprehensive overview of the capabilities of deep learning in property prediction, geometry generation, and inverse design of mechanical metamaterials. Additionally, I highlight the potential of leveraging deep learning to create universally applicable datasets, intelligently designed metamaterials, and material intelligence.
In the second article, I propose three multiphase metamaterials derived from triply periodic minimal surfaces. The multiphase metamaterials possess highly variable stiffness based on thermally-induced phase transition.
In the third article, I propose a new deep learning framework that can generate different and diverse material microstructures using text prompts.
I have published 6 peer-reviewed papers on international journals and 1 patent during this academic year.
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