Deep learning-based framework for the on-demand inverse design of metamaterials with arbitrary target band gap

This study presents a deep learning framework for the forward prediction of band structures and the inverse design of metamaterials. For forward prediction, a convolutional neural network is trained to estimate the bandgap width and mid-frequency of the bandgap of metamaterials. For inverse design, a conditional variational autoencoder (cVAE) network is employed, where band structure features (bandgap width and mid-frequency of the bandgap) serve as inputs, and the network predicts unit cell topologies. Once trained, the cVAE decoder enables the rapid design of metamaterials with user-defined bandgap properties, requiring no prior knowledge. Results show that this approach accurately predicts bandgap characteristics for topologies and efficiently generates designs tailored to target properties. The method accelerates metamaterial design and optimization, offering scalability to asymmetric 2D and 3D structures, and holds promise for driving innovation in metamaterial research.