We’ve introduced plasticity as a design tool to create metamaterials that are simultaneously lightweight, strong, and dissipative! Out in Nature!

Machine Materials Lab
We’ve introduced plasticity as a design tool to create metamaterials that are simultaneously lightweight, strong, and dissipative! Out in Nature!

In a PNAS article, we’ve introduced granular metamaterials made from auxetic particles. They are softer and flow more easily and less intermittently than ordinary granular packings!

We discover a way to achieve an achieve an endless domino effect! We show that non-reciprocity drives solitons and antisolitons towards the same direction. This allows us to send trains of solitons and antisolitons without having to manually reset the material. Out in Nature.

We introduce viscoelastic kirigami whose response depend on strain rate. We observe the emerge of diffusive kinks. We use those kinks to achieve basic mechanical tasks such as shape morphing, sensing and moving objects. Out in Nature Communications!
In a PNAS article, we have demonstrated a model-free method for identifying topology, enabling the discovery of new topological materials using a purely experimental approach.

We create metamaterials with built-in frustration, show that they are exhibit a topological property: non-orientable order. We further demontrate that non-orientable order helps to create programmable non-commutative response. Published in Nature.


Usually, materials are either stiff or can absorb vibrations well – but rarely both. Here, we exploit buckling to make metamaterials that are both stiff and good at absorbing vibrations. Out in Advanced Materials. See also this press release.

We discovered a range of nonlinear behaviors in elastoactive chains, including self-oscillations, self-snapping and synchronization. See paper in PRL.
![Elasto_active_highlight_3[56800].png](https://coulaislab.com/wp-content/uploads/2023/05/elasto_active_highlight_356800.png)
In our latest PRL we show that combinatorial metamaterials—think of complicated puzzle pieces fitting together to make desired shapes—can be classified efficiently and accurately by neural networks. This is surprising, since these desired shapes occur only for very few specific designs and are sensitive to even a single puzzle piece changing.