Design of auxetic metamaterials using deep learning

使用深度学习设计拉胀超材料

基本信息

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

项目摘要

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.

我专注于通过正向设计和逆向设计来设计机械超材料。在正向设计中，我提出了设计三种不同的可重编程柔性机械超材料的策略，它们具有不同应用的潜力，例如软机器人、驱动、自适应安全和运动器材。该策略可以轻松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架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 建筑材料的逆向设计，这些材料在骨植入和生物医学方面具有潜在的应用支架。逆向设计框架将消除各种应用中材料设计中的猜测，从而加速设计此类复杂建筑材料的革命。此外，其他研究人员也将从材料发现和开发方面的工作中获得灵感。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通过逆向设计获得目标材料。拉胀超材料逆向设计的代码已在MDR上共享，科学家可以获取生成超材料的代码具有其目标应用所需的机械性能。基于这项研究，我本学年在国际期刊上发表了 5 篇同行评审论文。

项目成果

期刊论文数量（0）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

My homepage

我的主页

DOI：
发表时间：
期刊：
影响因子：
0
作者：
通讯作者：

Nonionic Surfactant-Assisted In Situ Generation of Stable Passivation Protective Layer for Highly Stable Aqueous Zn Metal Anodes

非离子表面活性剂辅助原位生成高稳定水性锌金属阳极的稳定钝化保护层

DOI：
10.1021/acs.nanolett.2c03114
发表时间：
2022
期刊：
影响因子：
10.8
作者：
Zhang Yuanjun;Zheng Xiaoyang;Wu Kuan;Zhang Ying;Xu Gang;Wu Minghong;Liu Hua;Dou Shi;Wu Chao
通讯作者：
Wu Chao

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices

使用体素化 Voronoi 晶格对具有目标孔隙率和刚度的三维建筑多孔材料进行基于深度学习的逆向设计

DOI：
10.1080/14686996.2022.2157682
发表时间：
2023
期刊：
影响因子：
5.5
作者：
Zheng, Xiaoyang;Chen, Ta-Te;Jiang, Xiaoyu;Naito, Masanobu;Watanabe, Ikumu
通讯作者：
Watanabe, Ikumu

Regulating the Coordination Geometry and Oxidation State of Single‐Atom Fe Sites for Enhanced Oxygen Reduction Electrocatalysis

调节单原子 Fe 位点的配位几何和氧化态以增强氧还原电催化

DOI：
10.1002/smll.202300373
发表时间：
2023
期刊：
影响因子：
13.3
作者：
Wang Minjie;Wang Li;Li Qingbin;Wang Dan;Yang Liu;Han Yongjun;Ren Yuan;Tian Gang;Zheng Xiaoyang;Ji Muwei;Zhu Caizhen;Peng Lishan;Waterhouse Geoffrey I. N.
通讯作者：
Waterhouse Geoffrey I. N.

Reprogrammable flexible mechanical metamaterials

可重新编程的柔性机械超材料