Machine Learning for Metamaterial Design

There is no standard method to design mechanical metamaterials. Instead, one is limited by designer's intuition and the tuning of a few predefined design parameters. Here we show that machine learning overcomes this limitation by learning the relation between design and mechanical property, which we use to inverse design for desired mechanical properties. This allows us to consider and efficiently explore much larger design spaces, opening up new possibilities for complex design of (meta)materials.