To overcome the aforementioned limitations, we apply a combination of machine-learning techniques (i.e., high-dimensional agnostic descriptors of the molecular/atomic environments, unsupervised density-based data clustering and dimensionality reduction -see later on), aiming to obtain a characterization of the BTA fibers based on generally-applicable structural indicators and on an identification of defect states that relies exclusively on their (meta)stability. To minimize the risk of introducing prior bias in the choice of geometric descriptors, we decided to adopt the SOAP feature vectors, that characterize the local surrounding of each monomer core in a general and agnostic fashion, based on a discretized representation of three-body correlation functions (see Computational Methods). Once the structural sampling of the simulated systems is converged, the next step involves analyzing CG-MD simulations and mapping all the possible metastable structural patterns explored by the systems in terms of SOAP features. This allows to track all possible states visited by the monomer cores in the model fibers during the simulations. To do so, we choose a density-based clustering scheme, the probabilistic analysis of molecular motifs (PAMM). PAMM estimates the probability density with which different motifs appear, and identifies local probability maxima as significant (metastable) patterns. Thus, it allows to partition all the configurations for the monomer cores into a small number of clusters and to identify them in different simulations. While assessing the stability of clusters with respect to noise, PAMM can also clarify the hierarchical relations between motifs. In our analysis, SOAP vectors are computed by attaching one fictitious atom to the center of each BTA monomer. As better clarified below, by explicitly considering in the analysis only the CG atoms corresponding to the aromatic rings and amide groups in the monomers we make the representation transferable across the systems. This is important to measure defects in BTA supramolecular polymer variants in a comparable way (see next section). Preliminary tests showed clearly that the core of the monomer alone is a key discriminant of the variance in terms of order/disorder within these supramolecular fibers, which is well captured considering only the stacking of the aromatic rings. Further discussions on the minimum amount of structural details necessary to rationalize order/disorder in supramolecular polymers can be found in the Supporting Information (SI). The colors in Figure -d identify multiple metastable structural states in both fiber (1) and