Elucidating the Full Potential of OPV Materials Utilizing a High-Throughput Robot-Based Platform and Machine Learning

Du X, Lüer L, Heumüller T, Wagner J, Berger C, Osterrieder T, Wortmann J, Langner S, Vongsaysy U, Bertrand M, Li N, Stubhan T, Hauch J, Brabec C (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Joule Elsevier

DOI: 10.1016/j.joule.2020.12.013

Abstract

Evaluating the potential of organic photovoltaic (OPV) materials and devices for industrial production is a multidimensional optimization process with an incredibly large parameter space. Here, we demonstrate automated OPV material and device characterization in terms of efficiency and photostability. Gaussian process regression (GPR) prediction based on optical absorption features guided the optimization process with promising prediction accuracy for PV parameters and burn-in losses. With ∼100 process conditions, screening for efficiency and photostability can be finished within 70 h. The highest power conversion efficiency (PCE) of 14% was achieved by fully automated device fabrication in air with a model material system PM6:Y6. Improving molecular ordering has been identified as the most promising motif for further efficiency optimization. Thin active layers combined with medium thermal annealing temperature are favorable to simultaneously improve efficiency and suppress burn-in losses. The platform and protocol may be expanded to any solution-processed organic semiconductor and interface materials.

Authors with CRIS profile

Xiaoyan Du Institute Materials for Electronics and Energy Technology (i-MEET) (i-MEET) Larry Lüer Institute Materials for Electronics and Energy Technology (i-MEET) (i-MEET) Thomas Heumüller Institute Materials for Electronics and Energy Technology (i-MEET) (i-MEET) Jerrit Wagner Institute Materials for Electronics and Energy Technology (i-MEET) (i-MEET) Christian Berger Institute Materials for Electronics and Energy Technology (i-MEET) (i-MEET) Tobias Osterrieder Institute Materials for Electronics and Energy Technology (i-MEET) (i-MEET) Jonas Wortmann Institute Materials for Electronics and Energy Technology (i-MEET) (i-MEET) Stefan Langner Institute Materials for Electronics and Energy Technology (i-MEET) (i-MEET) Ning Li Institute Materials for Electronics and Energy Technology (i-MEET) (i-MEET) Jens Hauch Institute Materials for Electronics and Energy Technology (i-MEET) (i-MEET) Christoph Brabec Institute Materials for Electronics and Energy Technology (i-MEET) (i-MEET)

Additional Organisation(s)

Interdisziplinäres Zentrum, Center for Nanoanalysis and Electron Microscopy (CENEM) Graduiertenkolleg 1896/2 In situ Mikroskopie mit Elektronen, Röntgenstrahlen und Rastersonden

Involved external institutions

ARMOR Group FR France (FR) Sciprios GmbH DE Germany (DE)

How to cite

APA:

Du, X., Lüer, L., Heumüller, T., Wagner, J., Berger, C., Osterrieder, T.,... Brabec, C. (2021). Elucidating the Full Potential of OPV Materials Utilizing a High-Throughput Robot-Based Platform and Machine Learning. Joule. https://doi.org/10.1016/j.joule.2020.12.013

MLA:

Du, Xiaoyan, et al. "Elucidating the Full Potential of OPV Materials Utilizing a High-Throughput Robot-Based Platform and Machine Learning." Joule (2021).

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