Ceci n'est pas un committor, yet it samples like one: efficient sampling via approximated committor functions

TL;DR

This paper introduces a simplified, descriptor-based approximation of the committor function for enhanced sampling in atomistic simulations, achieving robust pathway exploration with reduced computational cost.

Contribution

It presents a new learning criterion that bypasses explicit coordinate gradients, maintaining sampling effectiveness while significantly lowering computational demands.

Findings

Retains robust sampling performance.

Reduces computational costs substantially.

Enables study of previously infeasible processes.

Abstract

Atomistic simulations are widely used to investigate reactive processes but are often limited by the rare event problem due to kinetic bottlenecks. We recently introduced an enhanced sampling approach based on the committor function, machine-learned following a variational principle. This method combines a transition-state-oriented bias potential, expressed as a functional of the committor, with a metadynamics-like bias along a committor-based collective variable, enabling uniform exploration of reaction pathways. In its original formulation, the committor is represented by a neural network that takes physical descriptors as input and is trained by minimizing a functional involving gradients with respect to atomic coordinates, which can be computationally demanding in some cases. Here, we propose a simplified learning criterion formulated entirely in the descriptor space, which bypasses the need for explicit and costly coordinate gradients and provides a relaxed upper bound to the original variational principle. Although this approach does not formally target the exact committor, we show that it retains robust sampling performance while significantly reducing computational costs, thus enabling the study of processes that would be practically unfeasible using the original formulation.