MolPackL: Quantification and Interpretation of Intermolecular Interactions Driven by Molecular Packing

In organic optoelectronic devices, the properties of the aggregated organic materials depend not only on individual molecules or monomers but also significantly on their packing modes. Different from their inorganic counterparts linked by explicit covalent bonds, organic solids exhibit intricate and numerous intermolecular interactions (IMIs). Due to the intrinsic complexity and disorder of IMIs, identifying and understanding them is a formidable challenge in experimental, theoretical, and data-driven approaches. In this work, we constructed an innovative algorithm framework, Molecular Packing Learning (MolPackL), which can accurately quantify elusive IMIs using contact density histograms (CDHs) and efficiently extract intermolecular features for further property prediction of organic solids. It performs satisfactorily in training predictive models of IMI-related properties in molecular crystals. Particularly, the band gap predictive model based on MolPackL achieved the best-reported performance, with an MAE of 0.20 eV and an impressive