  From self-assembly and protein folding to combinatorial metamaterials, a key
challenge in material design is finding the right combination of interacting
building blocks that yield targeted properties. Such structures are fiendishly
difficult to find; not only are they rare, but often the design space is so
rough that gradients are useless and direct optimization is hopeless. Here, we
design ultra rare combinatorial metamaterials capable of multiple desired
deformations by introducing a two-fold strategy that avoids the drawbacks of
direct optimization. We first combine convolutional neural networks with
genetic algorithms to prospect for metamaterial designs with a potential for
high performance. In our case, these metamaterials have a high number of
spatially extended modes; they are pluripotent. Second, we exploit this library
of pluripotent designs to generate metamaterials with multiple target
deformations, which we finally refine by strategically placing defects. Our
pluripotent, multishape metamaterials would be impossible to design through
trial-and-error or standard optimization. Instead, our data-driven approach is
systematic and ideally suited to tackling the large and intractable
combinatorial problems that are pervasive in material science.
