Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing

We present a natural language processing pipeline that was used to extract polymer solar cell property data from the literature and simulate various active learning strategies. While data-driven methods have been well established to discover novel materials faster than Edisonian trial-and-error approaches, their benefits have not been quantified. Our approach demonstrates a potential reduction in discovery time by approximately 75 %, equivalent to a 15 year acceleration in material innovation. Our pipeline enables us to extract data from more than 3300 papers which is ~5 times larger than similar data sets reported by others. We also trained machine learning models to predict the power conversion efficiency and used our model to identify promising donor-acceptor combinations that are as yet unreported. We thus demonstrate a workflow that goes from published literature to extracted material property data which in turn is used to obtain data-driven insights. Our insights include active learning strategies that can simultaneously optimize the material system and train strong predictive models of material properties. This work provides a valuable framework for research in material science.