Parallel Trajectory Splicing and the Value of Information

A range of specialized Molecular Dynamics (MD) methods have been developed in order to overcome the challenge of reaching longer timescales in systems that evolve through sequences of rare events. In this talk, we consider Parallel Trajectory Splicing (ParSplice) which works by generating large number of trajectory segments in parallel in such a way that they can later be assembled into a single statistically correct state-to-state trajectory, enabling parallel speedups up to the number of parallel workers. In practice, the ability for ParSplice to scale significantly improves when it is possible to predict where the trajectory will be found in the future. With this insight in mind, we develop a maximum likelihood transition model that is updated on the fly and show how the value of the information gained from generating a segment can rigorously be taken into consideration in order to improve performance.