Scalable Active Metamaterials for Shape-Morphing

TL;DR

This paper introduces a hierarchical, scalable framework for designing complex, shape-morphing metamaterials by decoupling macro- and micro-scale design processes, enabling efficient and programmable structures.

Contribution

It presents a novel hierarchical design approach that combines data-driven inverse design and constrained optimization to efficiently create aperiodic, programmable metamaterials.

Findings

Enables fast and scalable design of complex shape-morphing structures.

Decouples macro- and micro-scale design for improved computational efficiency.

Achieves programmable deformation with high accuracy in metamaterials.

Abstract

Shape-morphing metamaterials enable adaptive structures capable of complex functional deformations, with applications ranging from reconfigurable structures and soft robotics to medical devices. However, their design remains challenging due to an inherent trade-off between deformation programmability and computational scalability. Periodic architectures offer computational tractability but are limited in their programmability, whereas aperiodic metamaterials provide richer deformation spaces at the cost of substantially increased design complexity. To bridge this gap, we propose a scalable active metamaterial (SAM) design framework that decouples the design problem into two scales by exploiting the local deformation independence of units isolated by stiff structural members. At the macroscale, global shape deformation is determined by iteratively solving a constrained mesh optimization problem incorporating data-driven constraints. At the microscale, the local infill geometry is obtained through inverse design via either a conditional diffusion model or an adjustable search strategy. This hierarchical decomposition enables fast, accurate, and scalable design of aperiodic shape-morphing metamaterials, offering a new computational paradigm for the design of programmable material systems.