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Over the last two decades the organic solar cell community has synthesisedtens of thousands of novel polymers and small molecules in the search for anoptimum light harvesting material. These materials were often crudely evaluatedsimply by measuring the current voltage curves in the light to obtain powerconversion efficiencies (PCEs). Materials with low PCEs were quicklydisregarded in the search for higher efficiencies. More complex measurementssuch as frequency/time domain characterisation that could explain why thematerial performed as it did were often not performed as they were too timeconsuming/complex. This limited feedback forced the field to advance using amore or less random walk of material development and has significantly slowedprogress. Herein, we present a simple technique based on machine learning thatcan quickly and accurately extract recombination time constants and chargecarrier mobilities as a function of light intensity simply from light/dark JVcurves alone. This technique reduces the time to fully analyse a working cellfrom weeks to seconds and opens up the possibility of not only fullycharacterising new devices as they are fabricated, but also data mininghistorical data sets for promising materials the community has over looked.

Machine learning for ultra high throughput screening of organic solar cells: Solving the needle in the hay stack problem