Shomik V

- Research Program Mentor

Project ideas

Machine learning to optimize optical performance of solar cells

With the rise of large-scale materials databases such as the Materials Project and ICSD, a wealth of information is already available for many types of materials. However, these databases are often missing essential information about optical properties (such as absorption) as these calculations are expensive. One strategy is to use machine learning as a tool to speed up expensive calculations. This will allow high-throughput screening of large databases and identification of promising candidate materials for photovoltaic applications.

Novel applications of thermophotovoltaics

While conventional photovoltaic cells use light from the sun to generate electricity, another branch of photovoltaics, called thermophotovoltaics or TPV, use light from a heat source instead. This is beneficial as any unused light by the TPV cell can be reflected back to the heat source and reabsorbed, instead of lost. Recently, a surge of interest in TPV has pushed their efficiency above 40%, and models show efficiencies as high as 60% are feasible in the next few years. With these high efficiencies come boundless potential applications in power plant design, waste heat recovery, concentrated solar power, etc. This project would involve focusing on one such application and designing an efficient system that incorporates TPV cells.

Coding skills