In the classification step, DPA identifies peaks in the density as cluster centers, i.e., distinct conformers. This operation is equivalent to identifying local minima in the D-dimensional free energy surface. For this step, we use the set of reweighted, smoothed densities ρ * i . Moreover, to avoid every fluctuation in density from being identified as a distinct peak, the DPA classifier is set to merge clusters separated by a saddle point between the free energy basins (a conformational transition state) with free energy less than 1 kT higher than one of the cluster centers it connects. Once cluster centers have been determined, all remaining configurations are assigned membership to the same cluster as their nearest neighbor of higher density . With all configurations classified, clusters with a population smaller than 1% of the total sample are discarded to avoid spurious clusters identified from anomalously isolated configurations.