  Over the last two decades the organic solar cell community has synthesised
tens of thousands of novel polymers and small molecules in the search for an
optimum light harvesting material. These materials were often crudely evaluated
simply by measuring the current voltage curves in the light to obtain power
conversion efficiencies (PCEs). Materials with low PCEs were quickly
disregarded in the search for higher efficiencies. More complex measurements
such as frequency/time domain characterisation that could explain why the
material performed as it did were often not performed as they were too time
consuming/complex. This limited feedback forced the field to advance using a
more or less random walk of material development and has significantly slowed
progress. Herein, we present a simple technique based on machine learning that
can quickly and accurately extract recombination time constants and charge
carrier mobilities as a function of light intensity simply from light/dark JV
curves alone. This technique reduces the time to fully analyse a working cell
from weeks to seconds and opens up the possibility of not only fully
characterising new devices as they are fabricated, but also data mining
historical data sets for promising materials the community has over looked.
