Multicomponent crystals (MCCs) composed of organic residues enable the tuning and enhancement of material properties without requiring covalent modification of their components. However, the systematic classification of organic MCCs raises fundamental questions: which classes and subclasses can be identified, and how does chirality influence this classification?
The availability of thousands of organic MCC crystal structures in crystallographic databases, such as the Cambridge Structural Database (CSD), provides an extensive dataset for investigating their structural diversity. By applying network science to this large structural dataset, we aim to uncover the underlying principles governing MCC formation, develop approaches for predicting new MCCs, and map the cocrystal–salt transition. This network-based approach provides a framework for systematically exploring the diverse landscape of organic multicomponent crystals.




