  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.
