Project Description

This research explores the intersection of nanophotonics, metasurfaces, and artificial intelligence to develop next-generation solutions for renewable and energy-efficient technologies. Metasurfaces—planar arrays of subwavelength nanostructures—enable unprecedented control over the amplitude, phase, polarization, and spectral response of light. Their ability to manipulate electromagnetic fields with high spatial and spectral precision provides a powerful platform for overcoming the limitations of conventional optical systems.

The project focuses on leveraging metasurface-based designs to enhance light-matter interactions in energy-related applications, particularly in photovoltaics and thermal radiation management. By engineering optical responses such as broadband absorption, spectral selectivity, and angular robustness, metasurfaces can significantly improve energy conversion efficiency and reduce losses in optoelectronic devices.

A central component of this work is the use of inverse design methodologies driven by artificial intelligence and machine learning. Unlike traditional forward-design approaches based on iterative numerical simulations, inverse design frameworks employ machine learning and deep neural networks to directly map desired optical functionalities to optimal nanostructure geometries. This paradigm enables rapid discovery of non-intuitive, high-performance designs that meet multiple constraints simultaneously, accelerating the development of efficient photonic devices.

By integrating advanced computational design, nanofabrication, and experimental characterization, this research aims to establish a new class of multifunctional photonic materials capable of controlling energy generation, conversion, and emission within a unified platform. Ultimately, the work contributes to the broader goal of advancing sustainable energy technologies through the convergence of nanophotonics and artificial intelligence.