Classifying soft self-assembled materials via unsupervised machine learning of defects

Unlike molecular crystals, soft self-assembled fibers, micelles, vesicles, etc., exhibit a certain order in the arrangement of their constitutive monomers but also high structural dynamicity and variability. Defects and disordered local domains that continuously form-and-repair in their structures impart to such materials unique adaptive and dynamical properties, which make them, e.g ., capable to communicate with each other. However, objective criteria to compare such complex dynamical features and to classify soft supramolecular materials are non-trivial to attain. Here we show a data-driven workflow allowing us to achieve this goal. Building on unsupervised clustering of Smooth Overlap of Atomic Position (SOAP) data obtained from equilibrium molecular dynamics simulations, we can compare a variety of soft supramolecular assemblies via a robust SOAP metric. This provides us with a data-driven “defectometer” to classify different types of supramolecular materials based on the structural dynamics of the ordered/disordered local molecular environments that statistically emerge within them. Defects and disordered local domains in soft, self-assembled aggregates determine their dynamic and adaptive properties, and enable communication between entities, but characterizing and classifying such intricate dynamic behaviors is highly complex. Here, the authors report on a data-driven workflow to identify objective criteria for the comparison of complex dynamic features in soft supramolecular materials, deriving a data-driven ’defectometer’ that allows to classify soft self-assembled materials based on the structural dynamics of the ordered/disordered molecular environments that statistically emerge within them.