We followed the protocol outlined in the original work for clustering similar g 3 distributions together using unsupervised machine learning. 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 3584×3584 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 40 and 2, respectively. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) was used for clustering similar g 3 distributions together from the 2D data using an epsilon value of 3.5. 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.