In a typical trajectory set from REMD simulations of a potential foldamer, structures above the melting temperature will be characterized by large distances between data points, especially in the case of particle coordinate RMSD, and structures well below the melting temperature will be tightly distributed. Density-based clustering methods, which identify clusters of high density and discard low density regions as 'noise' points, are well suited to this application. In particular, we have chosen to use the Density Based Spatial Clustering of Applications with Noise (DBSCAN) method implemented in the scikit-learn python package. DBSCAN is highly efficient in that only one pass through the data is performed, and it does not require that the number of clusters be known a priori. This is in contrast to center-based methods such as K-means clustering, which aims to minimize the intra-cluster sum of squares against the cluster mean, or K-medoids, which aims to minimize the sum of distances between cluster member data and the medoid in each cluster. In general, the number of metastable states of a foldamer system is not known, making it necessary to repeat K-means or K-medoids clustering several times with different numbers of clusters to find an optimal number of clusters. DBSCAN uses two key parameters-the number of minimum samples (n min ) within the radius ( ) required to classify a data point as a 'core point'. Points that lie within of a core point, but do not satisfy the core point criteria, are non-core cluster members. Any point that lies beyond of a core point is classified as noise.