Harnessing disorder to decouple extension and shear in kirigami metamaterials

2026-07-17

AI Summary

This work addresses the challenge of parasitic shear and discretely tunable, coupled anisotropic stiffness in periodic kirigami metamaterials, which inherently exhibit strong coupling between tensile and shear deformations. Inspired by biological tissues, the study pioneers the deliberate incorporation of engineered disorder as a controllable design degree of freedom to transcend the limitations of periodicity. By integrating a geometry-aware graph neural network with a genetic algorithm, the authors achieve inverse design of nonlinear mechanical responses that effectively decouple tensile and shear behaviors. Fabricated disordered kirigami elastomer samples successfully reproduce the predicted continuously tunable and nearly fully decoupled anisotropic responses. The proposed graph neural network accelerates training by an order of magnitude while achieving higher accuracy, thereby establishing a closed-loop validation from computational design to physical realization.

Abstract

Kirigami turns stiff sheets into compliant, shape-morphing structures, but its reliance on periodic cut patterns comes at a cost: correlated panel rotations couple extension to shear, so stretching one axis drives a parasitic shear that cannot be suppressed, and also confine anisotropic stiffness to a narrow, discrete set of responses that cannot be tuned independently. Biological tissues overcome an analogous constraint through controlled disorder, such as graded fiber orientations in skin and hierarchical anisotropy in myocardium, achieving direction-dependent mechanics unavailable to regular architectures. Here, we show that engineered disorder is a design degree of freedom for kirigami, with stochastic kirigami accessing a continuous and far broader region of mechanical response than periodic patterns. This includes programmable anisotropy with near-complete elimination of extension-shear coupling. Because disordered patterns lack a simple parameterization, we navigate this design space with a geometry-aware graph neural network (GNN) that maps cut topology to the full nonlinear, bidirectional stress-strain response, coupled to a genetic algorithm that inverse-designs patterns reproducing target responses along two perpendicular axes. The GNN trains an order of magnitude faster and more accurately than image-based models. Fabricated elastomer samples reproduce the predicted nonlinear, anisotropic responses, closing the loop from design to physical component. By turning disorder into a variable to control directional stiffness, this work develops architected materials that stretch without parasitic shear, from soft actuators to tissue-interfacing devices matched to the anisotropy of living tissue.

Problem

• kirigami metamaterials
• extension-shear coupling
• mechanical anisotropy
• disorder
• parasitic shear

Innovation

• engineered disorder
• kirigami metamaterials
• extension-shear decoupling
• graph neural network
• inverse design