Exploiting model uncertainty to improve the scalability of long-time simulations using Parallel Trajectory Splicing

We consider parallel trajectory splicing (ParSplice), a specialized molecular dynamics method that extends simulation timescales through a parallel-in-time strategy, enabling parallel speedups proportional to the number of worker-processes deployed. In practice, the ability for ParSplice to scale significantly improves when it is possible to predict the future evolution of the atomistic trajectory. We propose improved predictive statistical models that are built on-the-fly in order to maximize computational efficiency. By imposing physical constraints and explicitly considering uncertainties in model estimation we show a significant improvement in the scalability of ParSplice, and hence a corresponding increase in the timescales that can be reached by direct simulation. (paper)