We compute an RMSD matrix using equation (3), where x i and x j are the coordinate matrices from structure i and j, respectively, x ik is the atom position vector for atom k index within structure i and N is the total number of atoms of each structure. Structures are aligned using MDTraj before calculating RMSDs, such that we are calculating minimum RMSD values between structures. The RMSD matrix was clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method implemented in scikit-learn to cluster the RMSD matrix of trajectory frames from our folding simulations. Using the clustering workflow we implemented in analyze_foldamers, we can quickly identify representative structures from the MC minimization with simulated annealing process. DBSCAN clustering relies on two hyperparameters, and min_samples. A data point with min_samples other samples within a distance defines a core point of a cluster. Data points within of core points without min_samples surrounding samples are considered neighbors of core points and are included in the cluster. Points outside of of core points are labeled as noise points. DBSCAN is a deterministic density-based clustering approach which has worked well for peptide systems and similar toy models. RM SD(x i ,