A paper published in the journal advanced photonics entitled "enhancing the light-object interaction in dielectric nanostructures through machine learning" shows that machine learning techniques can be used to enhance the surface of the element and optimize it for nonlinear optics and photomechanics. This discovery provides good possibilities for the development of a range of photonic devices and applications, including optical sensing, photoacoustic vibration and narrow-band filtering.
A metasurface is a multifunctional platform for controlling the scattering, color, phase or intensity of light and can be used for nanoscale light emission, detection, modulation, control and/or amplification. In recent years, because of the optical properties of metasurfaces, which can be adapted to various applications, including hyperlenses, tunable images and holograms, metasurfaces have become the focus of research.

1. schematic diagram of a series neural network for inverse design of enhanced Fano resonance metasurface (b) nonlinear generation and optical mechanical vibration.
   The work marks an exciting advance in nanophotonics, according to the senior photonics co-editor, SPIE researcher and head of the photonics and nanotechnology group at Anatoly Zayats, king's college London. "Optimizing metamaterials and metamaterials for specific applications is an important and time consuming issue," Zayats says. "With traditional methods, only a few parameters can be optimized, so the resulting performance is better than some other designs, but not necessarily the best." With machine learning, people can find the best design and cover the parameter space that cannot be covered by traditional methods.