  The combination of Markov state modeling (MSM) and molecular dynamics (MD)
simulations has been shown in recent years to be a valuable approach to unravel
the slow processes of molecular systems with increasing complexity. While the
algorithms for intermediate steps in the MSM workflow like featurization and
dimensionality reduction have been specifically adapted for MD data sets,
conventional clustering methods are generally applied for the discretization
step. This work adds to recent efforts to develop specialized density-based
clustering algorithms for the Boltzmann-weighted data from MD simulations. We
introduce the volume-scaled common nearest neighbor (vs-CNN) clustering that is
an adapted version of the common nearest neighbor (CNN) algorithm. A major
advantage of the proposed algorithm is that the introduced density-based
criterion directly links to a free-energy notion via Boltzmann inversion. Such
a free-energy perspective allows for a straightforward hierarchical scheme to
identify conformational clusters at different levels of a generally rugged
free-energy landscape of complex molecular systems.
