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. In this paper, chemometric and molecular modeling tools are applied to elucidate structure-performance relationships in small-molecules organic photovoltaics (SM-OPV). A combination of semiempirical calculated and 3D molecular interactions field (MIF)-based descriptors is used to describe training set and test set molecules, and partial least squares algorithm is applied to rationalize structure-photovoltaic activity relationships. In addition, a photovoltaic desirability function (PhotD) is also proposed as alternative and versatile tool for ranking potential candidates.
Quantitative structure-property relationship modeling of small organic molecules for solar cells applications
Tortorella, Sara;DE ANGELIS, Filippo;Cruciani, Gabriele
2018
Abstract
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. In this paper, chemometric and molecular modeling tools are applied to elucidate structure-performance relationships in small-molecules organic photovoltaics (SM-OPV). A combination of semiempirical calculated and 3D molecular interactions field (MIF)-based descriptors is used to describe training set and test set molecules, and partial least squares algorithm is applied to rationalize structure-photovoltaic activity relationships. In addition, a photovoltaic desirability function (PhotD) is also proposed as alternative and versatile tool for ranking potential candidates.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
