Prediction of power conversion efficiency of phenothiazine-based dye-sensitized solar cells using Monte Carlo method with index of ideality of correlation

ABSTRACT

Simplified molecular-input line-entry system (SMILES) notation and inbuilt Monte Carlo algorithm of CORAL software were employed to construct generative and prediction QSPR models for the analysis of the power conversion efficiency (PCE) of 215 phenothiazine derivatives. The dataset was divided into four splits and each split was further divided into four sets. A hybrid descriptor, a combination of SMILES and hydrogen suppressed graph (HSG), was employed to build reliable and robust QSPR models. The role of the index of ideality of correlation (IIC) was also studied in depth. We performed a comparative study to predict PCE using two target functions (TF₁ without IIC and TF₂ with IIC). Eight QSPR models were developed and the models developed with TF₂ was shown robust and reliable. The QSPR model generated from split 4 was considered a leading model. The different statistical benchmarks were computed for the lead model and these were $r_{\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}\mathrm{e}\mathrm{t}}^2=0.7784$; $r_{\mathrm{i}\mathrm{n}\mathrm{v}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{b}\mathrm{l}\mathrm{e}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}\mathrm{e}\mathrm{t}}^2=0.7955$; $r_{\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{b}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{e}\mathrm{t}}^2=0.7738$; $r_{\mathrm{v}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{d}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{e}\mathrm{t}}^2=0.7506$; $Q_{\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}\mathrm{e}\mathrm{t}}^2=0.7691$; $Q_{\mathrm{i}\mathrm{n}\mathrm{v}\mathrm{i}\mathrm{s}\mathrm{i}\mathrm{b}\mathrm{l}\mathrm{e}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}\mathrm{s}\mathrm{e}\mathrm{t}}^2=0.7850$; $Q_{\mathrm{c}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{b}\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{e}\mathrm{t}}^2=0.7501$; $Q_{\mathrm{v}\mathrm{a}\mathrm{l}\mathrm{i}\mathrm{d}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{s}\mathrm{e}\mathrm{t}}^2=0.7085$; IIC_{training set} = 0.8590; IIC_{invisible training set} = 0.8297; IIC_{calibration set} = 0.8796; IIC_{validation set} = 0.8293, etc. The promoters of increase and decrease of endpoint PCE were also extracted.

KEYWORDS:

• PCE
• phenothiazine
• CORAL
• QSPR
• IIC

Acknowledgements

The authors are thankful to Dr Andrey A. Toropov and Dr Alla P. Toropova for providing CORAL software. The authors are also thankful to the authorities of respective universities for providing infrastructure.

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this manuscript.

Data availability statement

The raw/processed data required to reproduce these findings are supplied in the supplementary files.

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2021.1973095