  Fundamental advances to increase the efficiency as well as stability of
organic photovoltaics (OPVs) are achieved by designing ternary blends which
represents a clear trend towards multi-component active layer blends. We report
the development of high-throughput and autonomous experimentation methods for
the effective optimization of multi-component polymer blends for OPVs. A method
for automated film formation enabling the fabrication of up to 6048 films per
day is introduced. Equipping this automated experimentation platform with a
Bayesian optimization, a self-driving laboratory is constructed that
autonomously evaluates measurements to design and execute the next experiments.
To demonstrate the potential of these methods, a four-dimensional parameter
space of quaternary OPV blends is mapped and optimized for photo-stability.
While with conventional approaches roughly 100 mg of material would be
necessary, the robot based platform can screen 2,000 combinations with less
than 10 mg and machine learning enabled autonomous experimentation identifies
the stable compositions with less than 1 mg.
