Quantitative structure-property relationship modeling of small organic molecules for solar cells applications

Despite the need of a reliable technology for solar energy harvesting, research on new materials for third generation photovoltaics is slowed down by the diffuse use of trial and error rather than rational material design approaches. The proposed study investigates the use of alternative strategies to material discovery inspired by drug design and molecular modeling. In particular, training set and test set (for validation purposes) comprising well-known small molecule-bulk heterojunction organic photovoltaics were built. Molecules were characterized by semiempirical calculated and 3D molecular interaction fields-based descriptors. Then partial least squares algorithm was applied to rationalize structure-photovoltaic activity relationships, and coefficients were investigated to clarify contributions played by the different molecular properties to the final performance. In addition, a photovoltaic desirability function (PhotD) was also proposed as alternative and versatile novel tool for ranking potential candidates. The partial least squares model and PhotD function were both internally and externally validated demonstrating their ability in estimating new candidates performances. The proposed approach demonstrates that, in the context of computational materials science, chemometrics and molecular modeling tools could effectively boost the discovery of novel promising candidates for photovoltaic application.