From high-dimensional committors to reactive insights

Transition path theory (TPT) offers a powerful formalism for extracting the rate and mechanism of rare dynamical transitions between metastable states. Most applications of TPT either focus on systems with modestly sized state spaces or use collective variables to try to tame the curse of dimensionality. Increasingly, expressive function approximators such as neural networks and tensor networks have shown promise in computing the central object of TPT, the committor function, even in very high-dimensional systems. That progress prompts our consideration of how one could use such a high-dimensional function to extract mechanistic insights. Here, we present and illustrate a straightforward but powerful way to track how individual dynamical coordinates evolve during a reactive event. The strategy, which involves marginalizing the reactive ensemble, naturally captures the evolution of the dynamical coordinate's distribution, not just its mean reactive behavior.