Machine Learning for Engineering Meta-Atoms with Tailored Multipolar Resonances

Wenhao Li, Hooman Barati Sedeh, Dmitrii Tsvetkov, Willie J. Padilla, Simiao Ren, Jordan Malof, Natalia M. Litchinitser

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
JournalLaser and Photonics Reviews
StateAccepted/In press - 2024


  • high-index nanoparticle
  • machine learning
  • Mie resonances
  • multipole decomposition
  • super scattering


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