We followed the protocol outlined in the original work for clustering similar g 3 distributions together using unsupervised ML. The final 50 ns (501 frames) of the simulation were used for the g 3 calculations. All other carbon atoms of the lipid acid moieties, except for the carbonyl carbon C4, were chosen for the independent analysis for each MMG molecule, thus giving the intramolecular structure for each lipid in isolation. The radial cut-off was set to 15 Å, with 201 radial bins and 101 angular bins. The similarities of the individual g 3 distributions were compared using a structural similarity index metric to create a similarity matrix. The dimensionality of the 10000×10000 similarity matrix was reduced to 2D with t-distributed stochastic neighbor embedding (t-SNE). t-SNE was randomly initialized, and the perplexity and early exaggeration were set to 100 and 4, respectively. Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) was used for clustering similar g 3 distributions together from the 2D data with the minimum cluster size parameter set to 50. The results were validated by confirming the spatial separation of the clusters also with independent dimensionality reduction using principal component analysis (PCA). It was possible to validate the results in this fashion since the t-SNE was randomly initialized.