Conventional human-driven methods face limitations in designing complex functional metasurfaces. Inverse design is poised to empower metasurface research by embracing fast-growing artificial intelligence. In recent years, many efforts have been devoted enriching inverse principles and applications. this perspective, we review most commonly used strategies including topology optimization, evolut...

We developed an inverse design framework, constrained crystal deep convolutional generative adversarial networks (CCDCGAN), enabling automated generation of stable multicomponent structures. Their formation energy can be optimized in the latent space based on reversible images with continuous representation. After training by 52,615 structures from Materials Project, CCDCGAN model is able to ge...

We here present a novel deep learning (DL) approach for designing structures of permanent magnets. The challenge the DL method in this kind problem is to learn mapping from desired magnetic field simple structure, i.e., an inverse design approach. demonstrate by training six different standard convolutional neural network (CNN) previously used ImageNet Large Scale Visual Recognition Challenge (...