  We present a simulation of various active learning strategies for the
discovery of polymer solar cell donor/acceptor pairs using data extracted from
the literature spanning $\sim$20 years by a natural language processing
pipeline. 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 for material discovery problems that can take
decades. 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 greater than 3300
papers which is $\sim$5 times larger and therefore more diverse 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 pipeline 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 be used to train
strong predictive models of material properties or be robust to the initial
material system used. This work provides a valuable framework for data-driven
research in materials science.
