A machine learning-based method to design modular metamaterials

Abstract

The concept of modular metamaterials and a machine learning-based method are introduced in this Letter. The method starts from selection of the structural bases based on the existing studies and then combines performance evaluation together with structural evolution to construct meta-atoms with specified properties. Both genetic algorithm and neural networks model are adopted to executed the designing process. Mechanical metamaterials with maximized bandgap and tunable bandgaps are demonstrated using the proposed method. This approach offers an effective means to design metamaterials. It is believed that the modular design of metamaterials based on machine learning is capable to construct meta-atoms with specific properties for metamaterials in various fields.