An international team of researchers has leveraged machine learning to develop perovskite solar cells with a near-record efficiency of 26.2%, approaching the current maximum of 26.7%. The team used an algorithm to identify 24 promising new hole-transporting materials (HTMs) from a dataset of over a million candidates. These materials play a critical role in transferring electron-hole pairs and significantly impact solar cell efficiency. The identified candidates were synthesized and tested in several iterations, refining the models and achieving highly efficient solar cells. The method demonstrates a scalable and efficient approach to discovering novel materials, paving the way for further advancements in solar energy technologies.
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Technical Consultant and EU Representative of Virtual Lab Inc., the parent company of the Materials Square platform
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Email: gabriele@simulation.re.kr
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