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Kim, Jin Young
Next Generation Energy Laboratory
Research Interests
  • Polymer solar cells, QD solar cells, organic-inorganic hybrid solar cells, perovskite solar cells, OLEDs, PeLEDs, organic FETs

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Machine learning-assisted development of organic photovoltaics via high-throughput in situ formulation

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Title
Machine learning-assisted development of organic photovoltaics via high-throughput in situ formulation
Author
An, Na GyeongKim, Jin YoungVak, Doojin
Issue Date
2021-06
Publisher
ROYAL SOC CHEMISTRY
Citation
ENERGY & ENVIRONMENTAL SCIENCE, v.14, no.6, pp.3438 - 3446
Abstract
The discovery of high-performance non-fullerene acceptors and ternary blend systems has resulted in a breakthrough in the efficiency of organic photovoltaics (OPVs) and has created new opportunities for commercialization. However, manufacturing technology has remained far behind expectations. Here we show a new research approach to develop OPVs via industrial roll-to-roll (R2R) slot die coating in conjunction with the in situ formulation technique and machine learning (ML) technology. The formulated PM6:Y6:IT-4F ternary blends deposited on continuously moving substrates resulted in the high-throughput fabrication of OPVs with various compositions. The system was used to produce training data for ML prediction. The composition/deposition parameters, referred to as deposition densities, and the efficiencies of 2218 devices were used to screen ML algorithms and to train an ML model based on a Random Forest regression algorithm. The generated model was used to predict high-performance formulations and the prediction was experimentally validated by fabricating 10.2% efficiency devices, the highest efficiency for R2R-processed OPVs so far.
URI
https://scholarworks.unist.ac.kr/handle/201301/52963
URL
https://pubs.rsc.org/en/content/articlelanding/2021/EE/D1EE00641J#!divAbstract
DOI
10.1039/d1ee00641j
ISSN
1754-5692
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