Spraying Better Solar Cells

Arti­ficial Intelli­gence 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.

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. (K. Norum, UCF)

These perovskites can be processed in solid or liquid state, offering a lot of flexi­bility. Imagine being able to spray or paint bridges, houses and sky­scrapers with the material, which would then capture light, turn it into energy and feed it into the elec­trical grid. Until now, the solar cell industry has relied on silicon because of its efficiency. But that’s old technology with limits. 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.

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, 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 bears out, it means researchers could identify the best formula to create a world standard. Then 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. “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.” (Source: UCF)

Reference: J. Li et al.: Perovskite Solar Cells: Predictions and Strategies Learned from Machine Learning to Develop High‐Performing Perovskite Solar Cells, Adv. En. Mat., online 12 December 2019; DOI: 10.1002/aenm.201970181

Link: NanoScience Technology Center, The College of Optics and Photonics, University of Central Florida, Orlando, USA

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