Machine Learning Zero Modes in Combinatorial Metamaterials

All around us, simple building blocks combine into complex systems with emergent properties. A general problem is to classify these properties into distinct classes. Statistical measures often play an important role, yet for many cases, the misalignment or perturbation of a single building block can have a dramatic effect on the collective properties. Here we show that Convolutional Neural Networks (CNNs) are able to accurately classify the design space of a combinatorial metamaterial, despite its sensitivity to small perturbations and the sparsity of the data set. Moreover, we show that in spite of the combinatorial explosion of the design space, the trained networks' accuracy remains high over a range of increasing design sizes. Together, our results show that neural networks are an excellent tool for combinatorial classification problems with complex underlying rules.