Design of auxetic metamaterials using deep learning

我专注于机械超材料的设计，包括正向设计和反向设计。在前向设计中，我提出了一种策略来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、自适应安全和运动器材。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，该策略为改变类似建筑的力学响应铺平了道路。在逆向设计中，我为二维拉伸超材料和三维建筑材料的逆向设计开发了一个深度学习框架，这两种材料在骨植入和生物医学支架方面具有潜在的应用。逆向设计框架将加速设计如此复杂的建筑材料的革命，因为它在各种应用中消除了材料设计中的猜测。此外，其他研究人员也将从这项工作中获得材料发现和开发方面的灵感。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从反设计中获得目标材料，伸展超材料逆设计代码已经在MDR上共享，科学家能够获得生成具有目标应用所需力学性能的超材料的代码。在这项研究的基础上，本学年我在国际期刊上发表了5篇同行评议论文。

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.