Researchers have used machine learning to design new polymers for organic photovoltaics (solar cells). After mining data from previous studies, they input the physical properties of polymers, together with the resulting solar cell efficiencies, into a Random Forest model to statistically predict the effectiveness of new materials. This informatics-based screening, combined with traditional knowledge-guided design, could vastly accelerate solar cell development compared with current trial-and-error experimentation.