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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.