Jose Luis Montaño Priede

Nanophotonics is a branch of science that studies the interaction between light and matter on a nanometric scale. This field aims to overcome the diffraction limit of light by creating surface plasmons, which couple the light with the conduction electrons of a metal. This technique enables the use of optics in various applications, such as high-resolution microscopy, ultra-high information storage, and miniaturization of optoelectronic devices.

When metallic objects have nanometric dimensions, the coupling of light with the electrons leads to the formation of localized surface plasmons (LSP). The intensity of this coupling phenomenon reaches its maximum at a specific wavelength known as localized surface plasmon resonance (LSPR), which is determined by the material, shape, size, and surrounding medium of the nanostructure. Materials such as gold and silver have been extensively used in the production of plasmonic nanostructures due to their LSPR occurring in the visible range of the spectrum.

Recently, the coupling of light-emitting materials with the plasmonic resonance of metallic nanostructures has been investigated to increase the emission rate of spontaneous emitters. This research has found that weak coupling increases the spontaneous emission rate effectively. However, in strong enough coupling, the system undergoes a Rabi split due to the creation of two hybridized energy states, known as plexcitons. The intermediate coupling gives rise to Fano-interference, which is the interference between a wide resonance (plasmon scattering) and a discrete one (exciton).

The study and control of electromagnetic systems, mainly plexcitons, are crucial from a technological point of view to develop applications such as quantum computing, sensors, lasers, pH meters, and non-linear devices. In hybrid metallic-semiconductor systems, the coupling between plasmon and exciton is studied using polyhedral metallic nanoparticles with immensely increased electric fields at their edges or vertices. Furthermore, photonic crystals on a mesoscopic scale have been created using periodically ordered metallic nanoparticles, in which their properties depend on the properties of the individual blocks.

Machine learning techniques, including neural networks and Bayesian optimization, are now playing a pivotal role in advancing nanophotonics and plasmonics. Neural networks, with their ability to model complex and nonlinear relationships, are being applied to optimize the design and synthesis of plasmonic nanostructures, predicting their optical properties with unprecedented accuracy. By training these models on extensive datasets of material characteristics and nanostructure configurations, researchers can rapidly explore a vast design space, identifying configurations that maximize desired properties such as localized surface plasmon resonance or Fano interference effects.

Bayesian optimization, a powerful tool for guiding experimental design, enables efficient exploration of this multi-dimensional design space by minimizing the number of costly physical experiments. In the synthesis of photonic and plasmonic nanostructures, Bayesian methods are used to iteratively refine and optimize the synthesis parameters, such as temperature, precursor concentration, and fabrication technique, to achieve the most favorable optical performance.

These machine learning-driven methods not only accelerate the discovery and optimization process but also enable the development of novel plasmonic materials and structures that were previously inaccessible through traditional trial-and-error experimentation. This integration of artificial intelligence with nanotechnology opens new horizons in the synthesis and application of nanophotonic devices, contributing to advancements in quantum computing, sensors, nanolasers, and other cutting-edge technologies.

Therefore, it is essential to have extensive knowledge in the synthesis and study of the optical properties of plexcitonic nanoparticles to develop new optical devices. The coupling between these basic systems in a periodic network could give rise to interesting optical phenomena, from nanometric systems to super-grids. Leveraging machine learning algorithms in these areas will further drive innovation and the creation of next-generation devices with enhanced optical capabilities.