A cascaded QSAR model for efficient prediction of overall power conversion efficiency of all-organic dye-sensitized solar cells

Abstract

A cascaded model is proposed to establish the quantitative structure-activity relationship (QSAR) between the overall power conversion efficiency (PCE) and quantum chemical molecular descriptors of all-organic dye sensitizers. The cascaded model is a two-level network in which the outputs of the first level (JSC, VOC, and FF) are the inputs of the second level, and the ultimate end-point is the overall PCE of dye-sensitized solar cells (DSSCs). The model combines quantum chemical methods and machine learning methods, further including quantum chemical calculations, data division, feature selection, regression, and validation steps. To improve the efficiency of the model and reduce the redundancy and noise of the molecular descriptors, six feature selection methods (multiple linear regression, genetic algorithms, mean impact value, forward selection, backward elimination, and +n-m algorithm) are used with the support vector machine. The best established cascaded model predicts the PCE values of DSSCs with a MAE of 0.57 (%), which is about 10% of the mean value PCE (5.62%). The validation parameters according to the OECD principles are R(2) (0.75), Q(2) (0.77), and Qcv2 (0.76), which demonstrate the great goodness-of-fit, predictivity, and robustness of the model. Additionally, the applicability domain of the cascaded QSAR model is defined for further application. This study demonstrates that the established cascaded model is able to effectively predict the PCE for organic dye sensitizers with very low cost and relatively high accuracy, providing a useful tool for the design of dye sensitizers with high PCE.

In silico designing of power conversion efficient organic lead dyes for solar cells using todays innovative approaches to assure renewable energy for future

“…Recently, QSPR model has been used to predict PCE values for different types of solar cell. [22][23][24][25][26] Venkatraman and Alsberg 22 suggested that photovoltaic properties such as PCE of phenothiazine dyes could be predicted by QSPR with the use of eigenvalue like descriptors. In our ongoing research, we have already successfully employed QSPR method to model and suggest improvements for PCE of polymer-based solar cells 23 and AOD for DSSCs explicit to cobalt electrolytes.…”

“…25 Venkatraman et al 26 investigated de novo computational design methodology to design of coumarin-based dye sensitizer with improved properties for use in DSSCs. Li et al 24 established a unique cascaded QSPR model to predict PCE of AOD for DSSCs. In this study, authors modeled a large number of dyes whose experimental values are not uniform as most of them are obtained from experiment for iodine electrolytes and few of them for cobalt electrolytes.…”

Correlation and redundancy on machine learning performance for chemical databases

“…This can also overcome the local minima problem. So SVM has become one of the most popular classification and regression methods due to its outperformance Extreme learning machine was proposed by Huang et al in 2006, and it has quickly developed in the past decade .…”

Power Conversion Efficiency of Arylamine Organic Dyes for Dye-Sensitized Solar Cells (DSSCs) Explicit to Cobalt Electrolyte: Understanding the Structural Attributes Using a Direct QSPR Approach

“…Li et al [23] suggested the importance of quantum-mechanical descriptors in the modeling of AOD. Therefore, the molecular geometries of Dye structures considered here were first prepared by molecular mechanics (MM).…”

“…Ip et al [22] performed successful prediction through in silico modeling of a set of new dyes on the basis of the known performance of existing dyes. In a recent work, Li et al [23] investigated organic dyes from diverse classes of chemicals to model PCE with a QSPR tool. They reported an acceptable cascade QSPR model to model PCE.…”