  The present paradigm in design and modelling of lattice architected
mechanical metamaterials is mostly limited to traditional numerical methods
like finite element analysis. Recently, the use of machine learning and
artificial intelligence techniques have become popular and here we extend these
ideas to architected metamaterials. We show that truss based lattices have a
natural resemblance to computational graphs which serve as an input for the
rapidly emerging field of graph neural networks (GNNs). A dataset comprising
thousands of such extremely complex lattices is trained using a GNN to predict
the underlying dominant deformation mechanism viz. stretching and bending. The
trained GNN achieves > 90% accuracy on a previously unseen complex lattice
dataset. Such graph-based learning of metamaterials has the capability to
predict a range of properties, from elastic moduli to fracture toughness and
promises AI driven discovery of emergent metamaterials possessing superlative
properties.
