Machine learning assisted design of shape-programmable 3D kirigami metamaterials

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.