Linear models, quantum molecular descriptors, and DSSC efficiency: an approach for evaluating potential new sensitizing dyes

The growing energy demand has accelerated the search for renewable energy sources, with dye-sensitized solar cells (DSSCs) emerging as promising candidates. To streamline the experimental process and reduce associated costs, we developed predictive models based on linear regression to predict the efficiency of DSSCs. These models, use quantum molecular descriptors (QMDs) derived from molecular electronics structure and excited states of the dyes. Our study focused on evaluating organic dyes based in imidazole, BODIPY, and squaraine for DSSC applications. The resulting linear models were simple, robust, and predictive, satisfying all standard validation metrics. Furthermore, each model's descriptors reveal key electronic/spectroscopic characteristics for efficiency enhancement, including: (i) increased molecular mass through branching, (ii) HOMO-LUMO gap control, and (iii) planar π-bridge optimization.