Machine Learning for Virtually Unlimited Solar Cell Experiments

Picture of a polymer:non-fullerene acceptor solar cell device, for which the polymer was designed by machine learning. Researchers at Osaka University use machine learning to design and virtually test molecules for organic solar cells, which can lead to higher efficiency functional materials for renewable energy applications. Osaka University researchers employed machine learning to design new polymers for use in photovoltaic devices. After virtually screening over 200,000 candidate materials, they synthesized one of the most promising and found its properties were consistent with their predictions. This work may lead to a revolution in the way functional materials are discovered.