Self-organizing maps as a data-driven approach to elucidate the packing motifs of perylene diimide derivatives

The efficient classification or prediction of crystal structures into a small number of families of related structures can be extremely important in the design of materials with specific packing and properties. In this respect, the traditional way to classify the crystal packing of organic semiconductors as herringbone, sandwich-herringbone, and beta- or gamma-sheets by visual inspection has its limitations. Herein, we present the use of a clustering method based on a combination of self-organizing maps and principal component analysis as a data-driven approach to classify different pi-stacking arrangements into families of similar crystal packing. We explored the pi-stacking arrangements within the crystal structures deposited in the Cambridge Structural Database of perylene diimide (PDI) derivatives with different types and positions of the substituents. The structures were characterised by a set of descriptors that were then used for classification. Six different packing families of PDIs were identified and their characteristics are discussed here. Finally, the effects of different substituent types and positions on the resulting packing arrangement are discussed.