From high-dimensional committors to reactive insights

TL;DR

This paper introduces a method to analyze high-dimensional transition path data by tracking the evolution of individual dynamical coordinates during reactive events, leveraging neural network approximations of committor functions.

Contribution

It presents a novel approach to extract mechanistic insights from high-dimensional systems using marginalization of the reactive ensemble and neural network-based committor functions.

Findings

Effective in high-dimensional systems

Captures distributional evolution of coordinates

Provides mechanistic insights into reactive events

Abstract

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 like 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 insight. 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.