QSPR modeling of absorption maxima of dyes used in dye sensitized solar cells (DSSCs)

Dye-sensitized solar cells (DSSCs) have recently received a significant attention as possible sources of renewable energy. As a result, a significant effort is being made to develop organic dyes for highly power conversion efficient DSSCs, in order to overcome the disadvantages of previous solar cell systems, such as cost reduction, weight reduction, and production methods that minimize environmental pollution. As shown by multiple recent research publications, computational techniques such as quantitative structure-property relationship (QSPR) modeling may aid in the development of suitable dyes for DSSCs satisfying many fundamental desired characteristics. The current report provides robust, externally verified QSPR models for five chemical classes of organic dyes (Triphenylamines, Phenothiazines, Indolines, Porphyrins and Coumarins) based on experimentally determined absorption maxima values. The size of the dye data points utilized to develop the models is the largest known to date. The QSPR models were constructed using only two-dimensional descriptors with clear physicochemical meaning. Using the best subset selection approach, we built 5, 3, 4, 3 and 2 descriptor models for the Triphenylamine, Phenothiazine, Indoline, Porphyrin and Coumarin classes, respectively. The models were validated both internally and externally, and then consensus predictions were made for specific categories of dyes using the developed partial least squares (PLS) models, and the Intelligent consensus predictor tool (http:// teqip.jdvu.ac.in/QSAR_Tools/) was used to determine whether the quality of test set compound predictions can be improved through the intelligent selection of multiple PLS models. We identified from the insights gained from the developed models several chemical attributes that are important in enhancing the absorption maxima. Thus, our study may be utilized to predict the lambda(max) values of novel or untested organic dyes and to give insights that will aid in the development of new dyes for use in solar cells with increased lambda(max) values and enhanced power conversion efficiency. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study presents robust QSPR models developed for five chemical classes of organic dyes (Triphenylamines, Phenothiazines, Indolines, Porphyrins and Coumarins) to predict absorption maxima values. These models were internally and externally validated, and consensus predictions were made for dye categories using an intelligent consensus predictor tool.