Predictive and generative machine learning models for photonic crystals

Abstract The prediction and design of photonic features have traditionally been guided by theory‐driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data‐driven methods for photonics. Here, we demonstrate several predictive and generative data‐driven approaches for the characterization and inverse design of photonic crystals. Concretely, we built a data set of 20,000 two‐dimensional photonic crystal unit cells and their associated band structures, enabling the training of supervised learning models. Using these data set, we demonstrate a high‐accuracy convolutional neural network for band structure prediction, with orders‐of‐magnitude speedup compared to conventional theory‐driven solvers. Separately, we demonstrate an approach to high‐throughput inverse design of photonic crystals via generative adversarial networks, with the design goal of substantial transverse‐magnetic band gaps. Our work highlights photonic crystals as a natural application domain and test bed for the development of data‐driven tools in photonics and the natural sciences.