Computing transition path theory quantities with trajectory stratification

TL;DR

This paper introduces a method to efficiently compute transition path theory quantities from trajectory data obtained via stratified sampling methods, overcoming backward and forward trajectory analysis challenges.

Contribution

It presents a simple data structure approach that enables transition path theory analysis on data from stratified sampling techniques like NEUS.

Findings

Efficient computation of TPT quantities from stratified trajectory data.

Applicable to methods like NEUS and other segment-sampling techniques.

Simplifies backward and forward trajectory analysis for TPT.

Abstract

Transition path theory computes statistics from ensembles of reactive trajectories. A common strategy for sampling reactive trajectories is to control the branching and pruning of trajectories so as to enhance the sampling of low probability segments. However, it can be challenging to apply transition path theory to data from such methods because determining whether configurations and trajectory segments are part of reactive trajectories requires looking backward and forward in time. Here, we show how this issue can be overcome efficiently by introducing simple data structures. We illustrate the approach in the context of nonequilibrium umbrella sampling (NEUS), but the strategy is general and can be used to obtain transition path theory statistics from other methods that sample segments of unbiased trajectories.