Computational Nanophotonics -How It Can Used To Design Novel Solar Cells With High Light Absorption-Electron Generation Efficiency

The adverse effects that fossil fuels have on the environment are now clearly being observed, for example, with melting polar ice caps, rising sea levels, record breaking atmospheric temperatures across the globe, terrible cyclones, massive forest fires and other natural disasters due to the effects of climate change. The amount of fossil fuels on Earth is limited and is continuously depleting. Additionally, the supply of fossil fuels to the global market is vulnerable to different geo-political events like the Russia-Ukraine conflict. Hence, there is an urgent need to find alternative sources of energy that are environmentally friendly, renewable and economically feasible. While a handful of such alternatives have been developed over the last few decades, none is as promising as solar energy. To that end, solar/photovoltaic cells have been developed to harness energy from the immense energy source that is the Sun. However, till date, solar cell efficiencies have been relatively poor that is preventing their widespread adoption as commercial alternatives to fossil fuels. To increase their efficiencies, solar cell efficiency must be improved in terms of optical (light) absorption and electron-hole (current) generation. One approach can alter the solar cell structures at the nanoscale-level to increase its efficiency. The goal of this presentation is to discuss how electromagnetic-field based computational techniques can be used to analyze light interactions with solar cells coupled to various metallic nanostructures and other novel nanomaterials that can improve their efficiencies. Additionally, the role of artificial intelligence/machine learning techniques to aid the electromagnetic computational techniques to optimize the physical parameters of the solar cells and nanostructures/nanomaterials will be discussed. It is hoped that this discussion will introduce the audience to the exciting new opportunities of merging different computational techniques with artificial intelligence/machine learning in order to design the new devices and technologies of the future that can create the “green” and “sustainable” energy to sustain life on Earth.