| Literature DB >> 32049386 |
Stefan Langner1, Florian Häse2,3,4,5, José Darío Perea1, Tobias Stubhan6, Jens Hauch6, Loïc M Roch2,3,4,5, Thomas Heumueller1, Alán Aspuru-Guzik3,4,5,7, Christoph J Brabec1,2.
Abstract
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 toward multicomponent active layer blends. The development of high-throughput and autonomous experimentation methods is reported for the effective optimization of multicomponent 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 4D parameter space of quaternary OPV blends is mapped and optimized for photostability. While with conventional approaches, roughly 100 mg of material would be necessary, the robot-based platform can screen 2000 combinations with less than 10 mg, and machine-learning-enabled autonomous experimentation identifies stable compositions with less than 1 mg.Keywords: high-throughput experimentation; machine learning; organic photovoltaics; photostability; solar energy
Year: 2020 PMID: 32049386 DOI: 10.1002/adma.201907801
Source DB: PubMed Journal: Adv Mater ISSN: 0935-9648 Impact factor: 30.849