The K-medoids algorithm finds the optimum number of clusters for 𝐸𝐸 "⃗ (𝑟𝑟) streamline distributions, as the number of clusters past which the within-cluster sum of squares does not improve. To quantify the success of the algorithm, we compute the silhouette score -a metric that measures how well-matched each point is to its assigned cluster, where a higher score is better. The algorithm optimally yields (Extended Data Fig. ) four to five unique 𝐸𝐸 "⃗ (𝑟𝑟) clusters for each system, with silhouette scores of 0.49, 0.34, 0.33, and 0.51 for Cytochrome C (Cyt C), wild-type (WT) CoLADH, mutated CoLADH, and HIV protease (HIV-P), respectively. These scores indicate distinguishable, well-separated clusters. We then visualized the identified clusters using multidimensional scaling of the pairwise distance matrix, with an example of WT Co-substituted LADH in Fig. . To ensure that the MD simulations captured a variety of 𝐸𝐸 "⃗ (𝑟𝑟) conformations and that clustering did not simply sort frames by their simulation replica, we also show multidimensional scaling graphs of individual MD runs, colored by cluster identity for Cyt C as an example (Extended Data Fig. ). We see here that creating a pairwise distance matrix, and clustering allows users to understand the underlying dynamics of 𝐸𝐸 "⃗ (𝑟𝑟), and partition MD trajectories by field for deeper analysis (Fig. , Extended Data Fig. ).