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 R 2 of 0.92. Class activation mapping (CAM) visually demonstrates MolPackL's accurate identification of effective interaction sites as the molecular packing changes. What is more, the elemental importance analysis verified that the superior score benefits from MolPackL's ability to comprehensively consider multiple influencing factors of IMIs. In summary, MolPackL provides a new framework for quantitative assessment and understanding of the effect of IMIs. The development of MolPackL marks a significant advancement in establishing predictive models of molecular aggregates, deepening the comprehension of IMIs on the material properties. Given the superior performance, we believe that MolPackL will also become a powerful tool in the design of high-performance organic optoelectronic materials.