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Monday, 16 Jul 2018

Osaka University researchers design organic photovoltaic polymers using machine learning

Machine learning could hugely accelerate solar cell development, since it instantaneously predicts results that would take months in the lab

Osaka University - Exploring new polymers for polymer solar cells using materials informatics

21 Jun 2018 | Editor

A research team from Osaka University have used machine learning to automate the search for polymer materials for organic photovoltaics devices. The work is expected to lead to vastly more efficient photovoltaic devices. The research has been published in The Journal of Physical Chemistry Letters.

According to the researchers informatics can make sense of large, complex datasets by detecting statistical trends that elude human experts.

The research team gathered data on 1,200 organic photovoltaics (OPVs) from around 500 studies. Using Random Forest machine learning, the researchers built a model combining the band gap, molecular weight, and chemical structure of these previous OPVs, together with their PCE, to predict the efficiency of potential new devices.

"The choice of polymer affects several properties, like short-circuit current, that directly determine the PCE"


"However, there's no easy way to design polymers with improved properties. Traditional chemical knowledge isn't enough. Instead, we used artificial intelligence to guide the design process."


Shinji Nagasawa, Lead author

Random Forest machine learning uncovered an improved correlation between the properties of the materials and their actual performance in OPVs.

To exploit this correlation, the model was used to automatically "screen" prospective polymers for their theoretical PCE. The list of top candidates was then whittled down based on chemical intuition about what can be synthesised in practice.

This strategy led the team to create a new, previously untested polymer.

The initial materials used to produce a practical OPV proved less efficient than expected. However, the model provided useful insights into the structure-property relationship. Its predictions could be improved by including more data, such as the polymers' solubility in water, or the regularity of their backbone.

"Machine learning could hugely accelerate solar cell development, since it instantaneously predicts results that would take months in the lab."


"It's not a straightforward replacement for the human factor - but it could provide crucial support when molecular designers have to choose which pathways to explore."


Akinori Saeki, co-author

Computer-Aided Screening of Conjugated Polymers for Organic Solar Cell: Classification by Random Forest

Shinji Nagasawa | Eman Al-Naamani | Akinori Saeki J. Phys. Chem. Lett. | 2018, 9 (10) | pp 2639–2646 DOI: 10.1021/acs.jpclett.8b00635

Abstract

Owing to the diverse chemical structures, organic photovoltaic (OPV) applications with a bulk heterojunction framework have greatly evolved over the last two decades, which has produced numerous organic semiconductors exhibiting improved power conversion efficiencies (PCEs). Despite the recent fast progress in materials informatics and data science, data-driven molecular design of OPV materials remains challenging. We report a screening of conjugated molecules for polymer–fullerene OPV applications by supervised learning methods (artificial neural network (ANN) and random forest (RF)). Approximately 1000 experimental parameters including PCE, molecular weight, and electronic properties are manually collected from the literature and subjected to machine learning with digitized chemical structures. Contrary to the low correlation coefficient in ANN, RF yields an acceptable accuracy, which is twice that of random classification. We demonstrate the application of RF screening for the design, synthesis, and characterization of a conjugated polymer, which facilitates a rapid development of optoelectronic materials.

www.osakafu-u.ac.jp   


About Osaka Prefecture University

Osaka University was founded in 1931 as one of the seven imperial universities of Japan and now has expanded to one of Japan's leading comprehensive universities. The University has now embarked on open research revolution from a position as Japan's most innovative university and among the most innovative institutions in the world according to Reuters 2015 Top 100 Innovative Universities and the Nature Index Innovation 2017. The university's ability to innovate from the stage of fundamental research through the creation of useful technology with economic impact stems from its broad disciplinary spectrum.

Source: Osaka Prefecture University


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