Here we show how machine learning approaches can be applied to tackle this problem and achieve a complete and automatic characterization of defects in a prototypical class of supramolecular polymers, based on 1,3,5-benzenetricarboxamide (BTA -Figure ), without prior assumptions of what exactly a defect is in these assemblies. BTA monomers self-assemble into one-dimensional fibers due to a combination of π-π stacking of the aromatic cores, threefold hydrogen bonding between the amide groups that surround the cores and solvophobic effects. Here we use a recentlydeveloped fine coarse-grained (CG) model that guarantees satisfactory sampling of these complex systems and good accuracy in the treatment of all the key interactions controlling the assembly. While allowing to study the structure and dynamics of BTA supramolecular polymers at sub-molecular resolution (<5 Å), this CG model recently provided some compelling preliminary evidence of the presence of defects in water-soluble BTA fiber variants. Nonetheless, the elusive and fleeting nature of defects in these dynamic systems demands for a more general and unambiguous procedure to identify and compare them. Our agnostic data-driven analysis relies on the Smooth Overlap of Atomic Potential (SOAP) framework in combination with the Probabilistic Analysis of Molecular Motifs (PAMM) to process trajectories (<5 Å resolution) obtained from unbiased molecular dynamics simulations of BTA supramolecular polymers and to describe the structural and dynamic motifs present in the supramolecular polymers in an automatic way. We obtain a complete description of how defects are continuously and spontaneously created and re-adsorbed in the structure of supramolecular fiber variants, along with qualitative mechanisms and pathways. This greatly improves our understanding of the internal structure and dynamics of supramolecular polymers, demonstrating how these assemblies cannot be simply seen as static structures or studied in terms of average properties. We provide detailed insights into the complex dynamic nature of these supramolecular fibers and demonstrate the capabilities of a general analysis tool that is also applicable to study other classes of dynamic self-assembled materials. This gives a preliminary qualitative evidence of defects. Data for fiber (1) are reported in purple for comparison (no defects appearing in this fiber). (c,d) Exterior (c) and interior (d) snapshots of a section of equilibrated fiber (2) from CG-MD simulation. The fiber surface is colored in grey, green spots (monomer cores) are clearly visible from the outside. These are defects in the stack (in green) that constitute "hot spots" from which monomer exchange originates in these fibers.