Fast Decorrelating Monte Carlo Moves for Efficient Path Sampling

Abstract

Many relevant processes in chemistry, physics, and biology are rare events from a computational perspective as they take place beyond the accessible time scale of molecular dynamics (MD). Examples are chemical reactions, nucleation, and conformational changes of biomolecules. Path sampling is an approach to break this time scale limit via a Monte Carlo (MC) sampling of MD trajectories. Still, many trajectories are needed for accurately predicting rate constants. To improve the speed of convergence, we propose two new MC moves, stone skipping and web throwing. In these moves, trajectories are constructed via a sequence of subpaths obeying super-detailed balance. By a reweighting procedure, almost all paths can be accepted. Whereas the generation of a single trajectory becomes more expensive, the reduced correlation results in a significant speedup. For a study on DNA denaturation the increase was found to be a factor 12.