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While spin angular momentum is limited to ±ℏ, orbital angular momentum (OAM) is, in principle, unbounded, enabling tailored optical transition rules in quantum systems. However, the large optical size of vortex beams hinders their coupling to nanoscale platforms such as quantum emitters. To address this challenge, we experimentally demonstrate the subdiffraction focusing of an OAM-carrying beam using a hypergrating, a flat meta-structure based on a multilayered hyperbolic composite. We show that our structure generates and guides high-wave vector modes to a deeply subwavelength spot and experimentally demonstrate the focus of an OAM-carrying beam on a spot size of ∼λ/3. We also show how the proposed platform facilitates the formation of an optical skyrmion with spin textures as small asλ/250, opening new avenues for controlling light–matter interactions. 

Abstract In the rapidly developing field of nanophotonics, machine learning (ML) methods facilitate the multi‐parameter optimization processes and serve as a valuable technique in tackling inverse design challenges by predicting nanostructure designs that satisfy specific optical property criteria. However, while considerable efforts have been devoted to applying ML for designing the overall spectral response of photonic nanostructures, often without elucidating the underlying physical mechanisms, physics‐based models remain largely unexplored. Here, physics‐empowered forward and inverse ML models to design dielectric meta‐atoms with controlled multipolar responses are introduced. By utilizing the multipole expansion theory, the forward model efficiently predicts the scattering response of meta‐atoms with diverse shapes and the inverse model designs meta‐atoms that possess the desired multipole resonances. Implementing the inverse design model, uniquely shaped meta‐atoms with enhanced higher‐order magnetic resonances and those supporting a super‐scattering regime of light‐matter interactions resulting in nearly five‐fold enhancement of scattering beyond the single‐channel limit are designed. Finally, an ML model to predict the wavelength‐dependent electric field distribution inside and near the meta‐atom is developed. The proposed ML based models will likely facilitate uncovering new regimes of linear and nonlinear light‐matter interaction at the nanoscale as well as a versatile toolkit for nanophotonic design.