Automated placement of interfaces in conformational kinetics calculations using machine learning.

Several recent implementations of algorithms for sampling reaction pathways employ a strategy for placing interfaces or milestones across the reaction coordinate manifold. Interfaces can be introduced such that the full feature space describing the dynamics of a macromolecule is divided into Voronoi (or other) cells, and the global kinetics of the molecular motions can be calculated from the set of fluxes through the interfaces between the cells. Although some methods of this type are exact for an arbitrary set of cells, in practice, the calculations will converge fastest when the interfaces are placed in regions where they can best capture transitions between configurations corresponding to local minima. The aim of this paper is to introduce a fully automated machine-learning algorithm for defining a set of cells for use in kinetic sampling methodologies based on subdividing the dynamical feature space; the algorithm requires no intuition about the system or input from the user and scales to high-dimensional systems.