Reactive Flux Matching: Mechanism Discovery and Adaptive Sampling of Rare Events
For researchers studying rare events in molecular systems, Flux Matching offers a method to extract mechanistic insight and improve sampling without requiring knowledge of underlying dynamics, though it is validated on small systems.
Flux Matching learns a current velocity and a scalar potential from reactive trajectory data to identify dominant reaction pathways and provide a data-driven reaction coordinate, enabling improved sampling of rare events. Validated on molecular systems, it yields accurate rate constant calculations.
Path sampling methods generate ensembles of reactive trajectories connecting metastable states, but extracting mechanistic insight from these data remains nontrivial. We introduce Flux Matching, a framework that learns two complementary objects directly from reactive trajectory data: a current velocity $u(z)$, whose streamlines trace the dominant reaction pathways, and a scalar potential $h(z)$, obtained from a weighted Helmholtz-Hodge decomposition of the reactive current, that serves as a data-driven reaction coordinate. Both minimize quadratic functionals over the reactive path ensemble, analogous to the flow matching loss in generative modeling, and require no knowledge of the underlying dynamics or stationary distribution. Unlike committor-based methods, $u$ and $h$ remain well-defined under projection onto non-Markovian collective variables, and their level sets in turn provide adaptive interfaces for improved sampling with enhanced sampling methods. Flux Matching is validated through the generation of current velocity trajectories and rate constant calculations on molecular systems.