Identifying and Tracking Defects in Dynamic Supramolecular Polymers

A central paradigm of self-assembly is to create ordered structures starting from molecularmonomers that spontaneously recognize and interact with each other via noncovalent interactions.In the recent years, great efforts have been directed toward reaching the perfection in thedesign of a variety of supramolecular polymers and materials with different architectures. Theresulting structures are often thought of as ideally perfect, defect-free supramolecular fibers,micelles, vesicles, etc., having an intrinsic dynamic character, which are typically studied at thelevel of statistical ensembles to assess their average properties. However, molecular simulationsrecently demonstrated that local defects that may be present or may form in these assemblies, and which are poorly captured by conventional approaches, are key to controlling their dynamicbehavior and properties. The study of these defects poses considerable challenges, as theflexible/dynamic nature of these soft systems makes it difficult to identify what effectively constitutesa defect, and to characterize its stability and evolution. Here, we demonstrate the powerof unsupervised machine learning techniques to systematically identify and compare defects insupramolecular polymer variants in different conditions, using as a benchmark 5°A-resolutioncoarse-grained molecular simulations of a family of supramolecular polymers. We shot that thisapproach allows a complete data-driven characterization of the internal structure and dynamicsof these complex assemblies and of the dynamic pathways for defects formation and resorption.This provides a useful, generally applicable approach to unambiguously identify defects inthese dynamic self-assembled materials and to classify them based on their structure, stabilityand dynamics.