Researchers at the University of Central Florida used Machine Learning (artificial intelligence) to optimize the materials used to make perovskite solar cells (PSC). Perovskites can be difficult to make as a usable and stable material for solar cells. Scientists have been trying to find just the right recipe to make them with all the benefits, and that's where artificial intelligence might come in for the rescue.

The team reviewed more than 2,000 peer-reviewed publications about perovskites and collected more than 300 data points then fed into the AI system they created. The system was able to analyze the information and predict which perovskites recipe would work best.

"Our results demonstrate that machine learning tools can be used for crafting perovskite materials and investigating the physics behind developing highly efficient PSCs," says Jayan Thomas, the study's lead author and an associate professor at the NanoScience Technology Center with multiple affiliations. "This can be a guide to design new materials as evidenced by our experimental demonstration."

If this model will prove itself useful, researchers could identify the best formula to create the most promising materials for solar cells. In that case, spray-on solar cells may happen in our lifetime, the researchers say.

"This is a promising finding because we use data from real experiments to predict and obtain a similar trend from the theoretical calculation, which is new for PSCs. We also predicted the best recipe to make PSC with different bandgap perovskites," says Thomas and his graduate student, Jinxin Li, who is the first author of this paper. "Perovskites have been a hot research topic for the past 10 years, but we think we really have something here that can move us forward."

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