The structural flexibility of organic semiconductors
offers vast
a search space, and many potential candidates (donor and acceptor)
for organic solar cells (OSCs) are yet to be discovered. Machine learning
is extensively used for material discovery but performs poorly on
extrapolation tasks with small training data sets. Active learning
techniques can guide experimentalists to extrapolate and find the
most promising D:A combination in a significantly small number of
experiments. This study uses an active learning technique with a predictive
random forest model to iteratively find the most optimal D:A combinations
in the search space using various acquisition functions. Active learning
results with five different acquisition functions (MM, MEI, MLI, MU,
and UCB) are compared. Results reveal that acquisition functions that
combine exploitation and exploration (MEI, MLI, and UCB) perform far
better than purely exploiting (MM) and purely exploring (MU) acquisition
functions. Interestingly, the proposed model can overcome the bottleneck
of extrapolating small training data sets and find most promising
D:A combinations in relatively fewer experiments.