Machine learning on performance of dye solar cells

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We studied the chemical structure and bandgap of 27 sensitizers, correlated to the literature reported PCEs while applying “in-between randomization”. Training and testing algorithms were carried out via 4 (dye) predictors including the number of π-bonds (PI), anchoring groups (X), HOMO(H)-LUMO(L), and bandgap energy (BG), with 2 responses for the possibility to achieve PCEs >1.82% (Yes/No).

The HLBG-input model’s analysis confirmed that BG is among the top controlling parameters that is ∼ 3-fold more important than H (HOMO) of the dye, indicating the absolute energy level (HOMO) is not as critical as BG for the dye absorption abilities in TiO2-based DSSCs.

This work shows the potential of adopting trained classifiers for analyzing natural sensitizer’s abilities to inject and separate generated electron-hole pairs for producing renewable, cost-effective, and sustainable energy.