  The rise of machine learning has fueled the discovery of new materials and,
especially, metamaterials--truss lattices being their most prominent class.
While their tailorable properties have been explored extensively, the design of
truss-based metamaterials has remained highly limited and often heuristic, due
to the vast, discrete design space and the lack of a comprehensive
parameterization. We here present a graph-based deep learning generative
framework, which combines a variational autoencoder and a property predictor,
to construct a reduced, continuous latent representation covering an enormous
range of trusses. This unified latent space allows for the fast generation of
new designs through simple operations (e.g., traversing the latent space or
interpolating between structures). We further demonstrate an optimization
framework for the inverse design of trusses with customized mechanical
properties in both the linear and nonlinear regimes, including designs
exhibiting exceptionally stiff, auxetic, pentamode-like, and tailored nonlinear
behaviors. This generative model can predict manufacturable (and
counter-intuitive) designs with extreme target properties beyond the training
domain.
