Finding the Right Bricks for Molecular Legos: A Data Mining Approach to Organic Semiconductor Design

Improving charge carrier mobilities in organic semiconductors is a challenging task that has hitherto primarily been tackled by empirical structural tuning of promising core compounds. Knowledge-based methods can greatly accelerate such local exploration, while a systematic analysis of large chemical databases can point toward promising design strategies. Here, we demonstrate such data mining by clustering an in-house database of >64,000 organic molecular crystals for which two charge-transport descriptors, the electronic coupling and the reorganization energy, have been calculated from first principles. The clustering is performed according to the Bemis–Murcko scaffolds of the constituting molecules and according to the side groups with which these molecular backbones are functionalized. In both cases, we obtain statistically significant structure–property relationships with certain scaffolds (side groups) consistently leading to favorable charge-transport properties. Functionalizing promising scaffolds with favorable side groups results in engineered molecular crystals for which we indeed compute improved charge-transport properties.