Kirigami-engineering has become an avenue for realizing multifunctional metamaterials that tap into the instability landscape of planar surfaces embedded with cuts. Recently, it has been shown that two-dimensional Kirigami motifs can unfurl a rich space of out-of-plane deformations, which are programmable and controllable across spatial scales. Notwithstanding Kirigami’s versatility, arriving at a cut layout that yields the desired functionality remains a challenge. Here, we introduce a comprehensive machine learning framework to shed light on the Kirigami design space and to rationally guide the design and control of Kirigami-based materials from the meta-atom to the metamaterial level. We employ a combination of clustering, tandem neural networks, and symbolic regression analyses to obtain Kirigami that fulfills specific design constraints and inform on their control and deployment. Our systematic approach is experimentally demonstrated by examining a variety of applications at different hierarchical levels, effectively providing a tool for the discovery of shape-shifting Kirigami metamaterials.

Kirigami-engineering 已经成为实现多功能超材料的途径，这些超材料利用了嵌入切口的平面表面的不稳定性。最近，已经表明二维 Kirigami 图案可以展开丰富的平面外变形空间，这些空间在空间尺度上是可编程和可控的。尽管 Kirigami 具有多功能性，但要达到产生所需功能的切割布局仍然是一个挑战。在这里，我们介绍了一个全面的机器学习框架，以阐明 Kirigami 设计空间，并合理地指导 Kirigami 基材料从元原子到超材料水平的设计和控制。我们采用聚类、串联神经网络的组合，和符号回归分析以获得满足特定设计约束并告知其控制和部署的 Kirigami。我们的系统方法通过检查不同层次级别的各种应用进行了实验证明，有效地为发现变形 Kirigami 超材料提供了工具。