Kavli Affiliate: Katia Bertoldi
| First 5 Authors: Giovanni Bordiga, Eder Medina, Sina Jafarzadeh, Cyrill Boesch, Ryan P. Adams
| Summary:
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.
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