Artificial Intelligence (AI) may be just the thing to accelerate spray-on solar cell technology, which could revolutionize how consumers use energy.
A research team at the University of Central Florida used Machine Learning, aka Artificial Intelligence to optimize the materials used to make perovskite solar cells (PSC). The Organic-Inorganic halide perovskites material used in PSC converts photovoltaic power into consumable energy.
These perovskites can be processed in solid or liquid state, offering a lot of flexibility.
Using perovskites, however, has one big barrier. They are difficult to make in a usable and stable material. Scientists spend a lot of time trying to find just the right recipe to make them with all the benefits—flexibility, stability, efficiency and low cost. That’s where artificial intelligence comes in.
UCF’s Jayan Thomas led the team in reviewing more than 2,000 peer-reviewed publications about perovskites and collecting more than 300 data points that were fed into the AI system the team created.
The system was able to analyze the information and predict which perovskites recipe would work best.
This is a promising finding because they used data from real experiments to predict and obtain a similar trend from the theoretical calculation, which is new for PSCs.
They also predicted the best recipe to make PSC with different bandgap perovskites.
The team’s work is so promising that its findings are the cover story Dec. 13 in the Advanced Energy Materials journal.
Reference- Advanced Energy Materials Journal, Futurism, Phys.Org website