Scalable Active Metamaterials for Shape-Morphing

2026-05-07

AI Summary

This work addresses the inherent trade-off between programmable deformability and computational scalability in shape-morphing metamaterials by proposing a dual-scale hierarchical design framework. The approach decouples local deformations through rigidly isolated unit cells and jointly optimizes macroscopic target shapes with microscopic infill geometries. Integrating constrained mesh optimization, conditional diffusion models, and an adaptive search strategy, the method establishes a data-driven inverse design pipeline that enables efficient and precise generation of aperiodic metamaterials. Experimental results demonstrate that the framework significantly enhances computational efficiency and scalability while preserving high deformation freedom, thereby facilitating the rapid synthesis of complex shape-morphing 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.

Problem

• shape-morphing metamaterials
• deformation programmability
• computational scalability
• aperiodic design
• active metamaterials

Innovation

• scalable active metamaterials
• shape-morphing
• hierarchical design
• inverse design
• data-driven optimization