2023 Fiscal Year Annual Research Report
Design of auxetic metamaterials using deep learning
| Project/Area Number |
22KJ0407
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| Allocation Type | Multi-year Fund |
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| Research Institution | National Institute for Materials Science |
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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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| Keywords | Material design / Mechanical metamaterial / Material informatics / Deep learning |
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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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