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Fundamental advances to increase the efficiency as well as stability oforganic photovoltaics (OPVs) are achieved by designing ternary blends whichrepresents a clear trend towards multi-component active layer blends. We reportthe development of high-throughput and autonomous experimentation methods forthe effective optimization of multi-component polymer blends for OPVs. A methodfor automated film formation enabling the fabrication of up to 6048 films perday is introduced. Equipping this automated experimentation platform with aBayesian optimization, a self-driving laboratory is constructed thatautonomously evaluates measurements to design and execute the next experiments.To demonstrate the potential of these methods, a four-dimensional parameterspace of quaternary OPV blends is mapped and optimized for photo-stability.While with conventional approaches roughly 100 mg of material would benecessary, the robot based platform can screen 2,000 combinations with lessthan 10 mg and machine learning enabled autonomous experimentation identifiesthe stable compositions with less than 1 mg.

Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multi-Component Systems