Accelerating materials discovery for polymer solar cells: Data-driven
  insights enabled by natural language processing
Pranav Shetty Aishat Adeboye Sonakshi Gupta Chao Zhang Rampi Ramprasad
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.