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The paper introduces a computationally cheaper variant of a committor-based enhanced sampling method for atomistic simulations, which addresses the rare event problem in molecular dynamics. They simplify the learning criterion for the committor function by formulating it entirely in descriptor space, avoiding expensive gradient calculations with respect to atomic coordinates. The resulting approximated committor function, while not exact, maintains robust sampling performance with significantly reduced computational cost.
Ditching explicit coordinate gradients slashes the computational cost of committor-based enhanced sampling, opening the door to simulating complex atomistic reactions previously out of reach.
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.
