title: 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.
authors.affiliation: School of Computer Science and Information Technology, Northeast Normal University, Changchun, 130117, China.
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Li
authors.forename: Ziyan
authors.initials: Z
authors.lastname: Zhong
authors.forename: Lin
authors.initials: L
authors.lastname: Li
authors.forename: Rui
authors.initials: R
authors.lastname: Gao
authors.forename: Jingxia
authors.initials: J
authors.lastname: Cui
authors.forename: Ting
authors.initials: T
authors.lastname: Gao
authors.forename: Li Hong
authors.initials: LH
authors.lastname: Hu
authors.forename: Yinghua
authors.initials: Y
authors.lastname: Lu
authors.forename: Zhong-Min
authors.initials: ZM
authors.lastname: Su
authors.forename: Hui
authors.initials: H
authors.lastname: Li
keywords: dye-sensitizer solar cells; machine learning methods; organic dye sensitizers; power conversion efficiency; quantitative structure activity relationship; quantum chemical calculations