Design of auxetic metamaterials using deep learning

使用深度学习设计拉胀超材料

• 批准号: 22KJ0407
• 负责人: ZHENG Xiaoyang
• 金额: $ 1.09万
• 依托单位: National Institute for Materials Science (2023)University of Tsukuba (2022)
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for JSPS Fellows
• 财政年份: 2023
• 资助国家: 日本
• 起止时间: 2023-03-08 至 2024-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-22KJ0407/
• 关键词: Mechanical metamaterial; Architected material; Materials and design; Deep learning; Finite element method; 3D printing; Inverse design

I focus on designing mechanical metamaterials by both forward design and inverse design. In the forward design, I proposed a strategy to design three different reprogrammable flexible mechanical metamaterials, which have the potential for different applications, such as soft robots, actuation, adaptive safety, and sports equipment. The strategy can be easily extended to other structures and smart materials. More importantly, this strategy paves the way to change the mechanical responses for similar architectures.In the inverse design, I developed a deep learning framework for the inverse design of 2D auxetic metamaterials and 3D architected materials, which have potential application for bone implants and biomedical stents. The inverse design framework will accelerate the revolution of designing such complicated architected materials by removing guesswork from material design in a variety of applications. In addition, other researchers will also get inspiration from the work with respect to materials discovery and development. For example, experimental researcher can get their targeted materials from the inverse design without any prior experience in materials design.The code of the inverse design of auxetic metamaterials has been shared on MDR, and scientists are able to get access to the code for generating metamaterials with their desired mechanical properties for their targeted applications. Based on this study, I have published 5 peer-reviewed papers on international journals during this academic year.

我专注于设计机械超材料的正设计和反设计。在正向设计中，我提出了一种策略，设计三种不同的可重编程柔性机械超材料，它们具有不同的应用潜力，如软机器人，驱动，自适应安全和运动设备。该策略可以很容易地扩展到其他结构和智能材料。更重要的是，这种策略为改变类似结构的机械响应铺平了道路。在逆向设计中，我开发了一个深度学习框架，用于2D拉胀超材料和3D结构材料的逆向设计，这些材料在骨植入物和生物医学支架方面具有潜在的应用。逆向设计框架将通过消除各种应用中材料设计的猜测来加速设计这种复杂结构材料的革命。此外，其他研究人员也将从材料发现和开发方面得到启发。例如，实验研究人员可以在没有任何材料设计经验的情况下通过逆设计获得目标材料。拉胀超材料的逆设计代码已在MDR上共享，科学家可以访问代码来生成具有所需机械性能的超材料，以满足其目标应用。基于这项研究，我在本学年在国际期刊上发表了5篇同行评审论文。

项目成果

Towards Stable Sodium Metal Battery with High Voltage Output through Dual Electrolyte Design

• DOI: 10.1016/j.ensm.2022.03.040
• 发表时间: 2022-03
• 期刊: Energy Storage Materials
• 影响因子: 20.4
• 作者: Ming Zhu;Xiaoyang Zheng;Lulu Li;Xiaolong Zhu;Zhongyi Huang;Guanyao Wang;Yuanjun Zhang;Haoran Liu

My homepage

我的主页

Reprogrammable flexible mechanical metamaterials

• DOI: 10.1016/j.apmt.2022.101662
• 发表时间: 2022-12
• 期刊: Applied Materials Today
• 影响因子: 8.3
• 作者: Xiaoyang Zheng;K. Uto;Wei-Hsun Hu;Ta-Te Chen;M. Naito;I. Watanabe