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Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multicomponent Systems.
Langner, Stefan; Häse, Florian; Perea, José Darío; Stubhan, Tobias; Hauch, Jens; Roch, Loïc M; Heumueller, Thomas; Aspuru-Guzik, Alán; Brabec, Christoph J.
Afiliación
  • Langner S; Institute of Materials for Electronics and Energy Technology (i-MEET), Department of Materials Science and Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Martensstrasse 7, Erlangen, 91058, Germany.
  • Häse F; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA.
  • Perea JD; Department of Chemistry, University of Toronto, Toronto, ON, M5S 3H6, Canada.
  • Stubhan T; Department of Computer Science, University of Toronto, Toronto, ON, M5S 3H6, Canada.
  • Hauch J; Vector Institute for Artificial Intelligence, Toronto, ON, M5S 1M1, Canada.
  • Roch LM; Institute of Materials for Electronics and Energy Technology (i-MEET), Department of Materials Science and Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Martensstrasse 7, Erlangen, 91058, Germany.
  • Heumueller T; Forschungszentrum Jülich GmbH, Helmholtz-Institut Erlangen-Nürnberg for Renewable Energy (IEK-11), Immerwahrstraße 2, Erlangen, 91058, Germany.
  • Aspuru-Guzik A; Forschungszentrum Jülich GmbH, Helmholtz-Institut Erlangen-Nürnberg for Renewable Energy (IEK-11), Immerwahrstraße 2, Erlangen, 91058, Germany.
  • Brabec CJ; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, 02138, USA.
Adv Mater ; 32(14): e1907801, 2020 Apr.
Article en En | MEDLINE | ID: mdl-32049386
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.
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Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Adv Mater Asunto de la revista: BIOFISICA / QUIMICA Año: 2020 Tipo del documento: Article País de afiliación: Alemania

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Adv Mater Asunto de la revista: BIOFISICA / QUIMICA Año: 2020 Tipo del documento: Article País de afiliación: Alemania