期刊
ADVANCED MATERIALS
卷 32, 期 14, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.201907801
关键词
high-throughput experimentation; machine learning; organic photovoltaics; photostability; solar energy
类别
资金
- Bavarian State Ministry of the Environment and Consumer Protection as part of the research project Umweltfreundliche Hocheffiziente Organische Solarzellen (UOS) [64845]
- Deutsche Forschungsgemeinschaft (DFG) [PN 182849149]
- DFG [BR 4031/9-1, INST 90/917-1 FUGG]
- Colombian Agency COLCIENCIAS
- Herchel Smith Graduate Fellowship
- Jacques-Emile Dubois Student Dissertation Fellowship
- Tata Sons Limited Alliance Agreement [A32391]
- Natural Resources Canada
- Canada 150 Research Chairs program
- Bavarian State Government [44-6521a/20/4]
- Aufbruch Bayern initiative of the state of Bavaria
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.
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