Machine Learning for Mie-Tronics

Electromagnetic multipole expansion theory underpins nanoscale light-matter interactions, particularly within subwavelength meta-atoms, paving the way for diverse and captivating optical phenomena. While conventionally brute force optimization methods, relying on the iterative exploration of various geometries and materials, are employed to obtain the desired multipolar moments, these approaches are computationally demanding and less effective for intricate designs. In this study, we unveil the potential of machine learning for designing dielectric meta-atoms with desired multipolar moments up to the octupole terms. Specifically, we develop forward prediction models to unravel the intricate relationship between the scattering response and the topological attributes of individual meta-atoms, and an inverse design model to reconstruct scatterers with the targeted multipolar moments. Utilizing a tandem network trained to tailor dielectric meta-atoms for generating intended multipolar moments across a broad spectral range, we further demonstrate the generation of uniquely shaped meta-atoms for exciting exclusive higher order magnetic response and establishing super-scattering regime of light-matter interaction. We also illustrate the accurate prediction of electric field distributions within the given scatterer. Our versatile methodology can be readily applied to existing datasets and seamlessly integrated with various network architectures and problem domains, making it a valuable tool for the design of different platforms at nanoscale.Comment: 19 pages, 5 figures, 1 tabl

Optimization of colour generation from dielectric nanostructures using reinforcement learning

Recently, a novel machine learning model has emerged in the field of reinforcement learning known as deep Q-learning. This model is capable of finding the best possible solution in systems consisting of millions of choices, without ever experiencing it before, and has been used to beat the best human minds at complex games such as, Go and chess, which both have a huge number of possible decisions and outcomes for each move. With a human-level intelligence, it has solved the problems that no other machine learning model has done before. Here, we show the steps needed for implementing this model to an optical problem. We investigate the colour generation by dielectric nanostructures and show that this model can find geometrical properties that can generate much purer red, green and blue colours compared to previously reported results. The model found these results in 9000 steps from a possible 34.5 million solutions. This technique can easily be extended to predict and optimise the design parameters for other optical structures. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement11Ysciescopu