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Emerging multi-material 3D printing techniques have paved the way for therational design of metamaterials with not only complex geometries but alsoarbitrary distributions of multiple materials within those geometries. Varyingthe spatial distribution of multiple materials gives rise to many interestingand potentially unique combinations of anisotropic elastic properties. Whilethe availability of a design approach to cover a large portion of all possiblecombinations of elastic properties is interesting in itself, it is even moreimportant to find the extremely rare designs that lead to highly unusualcombinations of material properties (e.g., double-auxeticity and high elasticmoduli). Here, we used a random distribution of a hard phase and a soft phasewithin a regular lattice to study the resulting anisotropic mechanicalproperties of the network in general and the abovementioned rare designs inparticular. The primary challenge to take up concerns the huge number of designparameters and the extreme rarity of such designs. We, therefore, usedcomputational models and deep learning algorithms to create a mapping from thespace of design parameters to the space of mechanical properties, thereby (i)reducing the computational time required for evaluating each designand (ii)making the process of evaluating the different designs highly parallelizable.Furthermore, we selected ten designs to be fabricated using polyjetmulti-material 3D printing techniques, mechanically tested them, andcharacterized their behavior using digital image correlation (DIC, 3 designs)to validate the accuracy of our computational models. The results of oursimulations show that deep learning-based algorithms can accurately predict themechanical properties of the different designs, which match the variousdeformation mechanisms observed in the experiments.

Deep learning for the rare-event rational design of 3D printed multi-material mechanical metamaterials