Researchers use machine learning to develop a process for finding optimal materials for perovskite solar cells
A team of researchers from Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), the Helmholtz Institute Erlangen-Nürnberg for Renewable Energy (HI ERN) and the Karlsruhe Institute of Technology (KIT) have developed a closed process for finding optimal high-performance materials for perovskite solar cells (PSC). The approach introduced in the study combines computer-assisted modeling, autonomous synthesis platforms and quantum theoretical calculations for characterizing molecules in order to be able to predict suitable material combinations and to test them in an automated fashion.
“What is the best way to find new materials for photovoltaic components that already have the optimal properties for this application?” This is the question that Prof. Christoph Brabec, speaker of the FAU Profile Center Solar and Chair of Materials Science and Engineering, has spent over a year exploring together with 22 researchers from the disciplines of chemistry, materials science and engineering, computer science and electrical engineering.