Issue 6, 2021

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

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.

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

Supplementary files

Article information

Article type
Paper
Submitted
02 Marts 2021
Accepted
23 Apr. 2021
First published
24 Apr. 2021

Energy Environ. Sci., 2021,14, 3438-3446

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

N. G. An, J. Y. Kim and D. Vak, Energy Environ. Sci., 2021, 14, 3438 DOI: 10.1039/D1EE00641J

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements