Researchers Hope AI Will Increase Perovskite Solar Cell Manufacturing
CAMBRIDGE, MA and STANFORD, CA—A team of MIT and Stanford University researchers have developed a machine-learning-based system that increases the feasibility of manufacturing perovskite solar cells, which have an energy conversion efficiency of 18.5 percent. Compared to photovoltaic cells, perovskite ones are much thinner and lighter, cheaper and easier to transport and install, and can be made with ultra-high throughput at room temperature instead of at hundreds of degrees.
The research team includes MIT professor of mechanical engineering Tonio Buonassisi, Stanford professor of materials science and engineering Reinhold Dauskardt and several others. Their production system uses the mathematical technique Bayesian Optimization to integrate data from prior experiments and information based on personal observations by experienced workers into the machine learning process. This results in more-accurate outcomes.