  Lattice-based mechanical metamaterials are known to exhibit quite a unique
mechanical behavior owing to their rational internal architecture. This
includes unusual properties such as a negative Poisson's ratio, which can be
easily tuned in reentrant-hexagonal metamaterials by adjusting the angles
between beams. However, changing the angles also affects the overall dimensions
of the unit cell. We show that by replacing traditional straight beams with
curved ones, it is possible to control Poisson's ratio of reentrant-hexagonal
metamaterials without affecting their overall dimensions. While the mechanical
properties of these structures can be predicted through finite element
simulations or, in some cases, analytically, many applications require to
identify architectures with specific target properties. To solve this inverse
problem, we introduce a deep learning framework for generating metamaterials
with desired properties. By supplying the generative model with a guide
structure in addition to the target properties, we are not only able to
generate a large number of alternative architectures with the same properties,
but also to express preference for a specific shape. Deep learning predictions
together with experimental measurements prove that this approach allows us to
accurately generate unit cells fitting specific properties for curved-beam
metamaterials.
