Automated discovery of reprogrammable nonlinear dynamic metamaterials

TL;DR

This paper presents an inverse-design framework for creating reprogrammable nonlinear mechanical metamaterials with tailored dynamic responses, enabling functionalities like energy focusing, splitting, and switching between tasks.

Contribution

The authors develop a differentiable simulation-based inverse-design method for reprogrammable nonlinear metamaterials, allowing automatic discovery of architectures for multiple dynamic functions.

Findings

Designed metamaterials successfully demonstrate targeted nonlinear dynamic behaviors.

Experimental tests confirm the robustness and reprogrammability of the designed architectures.

Framework enables switching between different dynamic tasks using static pre-compression.

Abstract

Harnessing the rich nonlinear dynamics of highly-deformable materials has the potential to unlock the next generation of functional smart materials and devices. However, unlocking such potential requires effective strategies to spatially design optimal material architectures for desired nonlinear dynamic responses such as guiding of nonlinear elastic waves, energy focusing, and cloaking. Here, we introduce an inverse-design framework for the discovery of flexible mechanical metamaterials with a target nonlinear dynamic response. The desired dynamic task is encoded via optimal tuning of the full-scale metamaterial geometry through an inverse-design approach powered by a custom-developed fully-differentiable simulation environment. By deploying such strategy, we design mechanical metamaterials tailored for energy focusing, energy splitting, dynamic protection, and nonlinear motion conversion. Furthermore, we illustrate that our design framework can be expanded to automatically discover reprogrammable architectures capable of switching between different dynamic tasks. For instance, we encode two strongly competing tasks -- energy focusing and dynamic protection -- within a single architecture, utilizing static pre-compression to switch between these behaviors. The discovered designs are physically realized and experimentally tested, demonstrating the robustness of the engineered tasks. All together, our approach opens an untapped avenue towards designer materials with tailored robotic-like reprogrammable functionalities.