The core of each micelle consists primarily of MA blocks. Figures While the MA monomers are key in the formation and stability of the micelles, the conformations that each topology of the polymers take within the micelle is significantly different. To investigate the specific conformations that different polymers adopt within a micelle, we applied a two step machine learning protocol: 44 dimensionality reduction using the Uniform Manifold Approximation and Projection (UMAP) algorithm, 45 followed by clustering in the resulting embedded space using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) (see the SI section: 'Dimensionality reduction and clustering' for the full methodology and results of this protocol). In each embedding, three clusters were identified representing the different groupings of similar conformations taken by each polymer (see Figure ). In each case, there is less than 8% of the data that is not clustered by HDBSCAN, which is shown in the bar charts in gray. Figure shows the probability distribution of each cluster in the various micelles as well as representative structures of each cluster of conformations for each polymer. The representative structures show that the conformations are clearly differentiable by the relative extension of the EO and MA blocks.