EurekAlert March1, 2021
Researchers in Japan used machine learning to screen hundreds of thousands of donor: acceptor pairs based on an algorithm trained with data from previously published experimental studies. Trying all possible combinations of 382 donor molecules and 526 acceptor molecules resulted in 200,932 pairs that were virtually tested by predicting their energy conversion efficiency. Basing the construction of our machine learning model on an experimental dataset drastically improved the prediction accuracy. To verify this method, one of the polymers predicted to have high efficiency was synthesized in the lab and tested. Its properties were found to conform with predictions, which gave the researchers more confidence in their approach. The research may contribute to the development of highly efficient organic solar cells and can be adapted to material informatics of other functional materials…read more. TECHNICAL ARTICLE