Elucidating the Full Potential of OPV Materials Utilizing a High-Throughput Robot-Based Platform and Machine Learning
(چکیده مقاله) :
Abstract :
Evaluating the potential of organic photovoltaic (OPV) materials
and devices for industrial production is amultidimensional 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.
and devices for industrial production is amultidimensional 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.
(توضیحات تکمیلی) :
(توضیحات تکمیلی) :
Description :
مقاله ISI انگلیسی اصلی
سال انتشار: 2021
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 13 صفحه
سال انتشار: 2021
فایل ISI انگلیسی اصلی ، با فرمت Pdf
تعداد صفحات فایل ISI انگلیسی اصلی: 13 صفحه
Authors / Descriptions(نویسندگان/توضیحات): مقاله ISI سال انتشار: 2021 / نویسندگان: Xiaoyan Du,* Larry Lu¨ er,1,6 Thomas Heumueller,1,2 Jerrit Wagner,1,2 Christian Berger,1,2 Tobias Osterrieder,2 Jonas Wortmann,2 Stefan Langner,1,2 Uyxing Vongsaysy,3 Melanie Bertrand,3 Ning Li,1,2 Tobias Stubhan,4 Jens Hauch,1 and Christoph J. Brabec
Sent date(تاریخ ارسال) :
1400/01/12 | 4/1/2021
Number of visits(تعداد بازدید):
450
Key words (کلمات کلیدی):
Machine Learning , Robot-Based Platform
Number of pages(تعداد صفحات) :
13
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