Machine Learning of Mechanisms in Combinatorial Metamaterials

Combinatorial metamaterials are metamaterials designed by combining fundamental building blocks, unit cells, picked from a discrete set. This discretized design space allows us to explore the limitless structural complexity of metamaterials in a controlled manner. However, analytical and conventional numerical approaches have difficulty in efficiently navigating this large design space, which grows exponentially with system size. Here we employ machine learning techniques to explore combinatorial metamaterial designs. We show that a trained convolutional neural network is able to classify never before seen configurations into those supporting system-wide periodic deformations in one dimension, line modes, and those who do not with over 99% accuracy. This suggests that the network has correctly learned to identify the set of design rules for the presence of a line mode. To study the scalability of this long-correlation classification problem, we investigate the relation between network complexity and configuration size. Our work provides insight into application and scaling of neural networks with regard to complex discrete structure-property maps.