Forward-Flux Sampling with Jumpy Order Parameters

TL;DR

This paper introduces a generalized forward-flux sampling method that relaxes the smoothness requirement of order parameters, enabling more accurate rate calculations for phenomena like crystallization where traditional methods struggle.

Contribution

The authors develop a new FFS algorithm that removes the need for smooth order parameters, improving rate estimation accuracy for aggregation phenomena.

Findings

Conventional FFS can underestimate nucleation rates by several orders of magnitude.

The generalized FFS performs well on systems with jumpy order parameters.

The new method broadens the applicability of FFS to complex systems.

Abstract

Forward-flux sampling (FFS) is a path sampling technique that has gained increased popularity in recent years, and has been used to compute rates of rare event phenomena such as crystallization, condensation, hydrophobic evaporation, DNA hybridization and protein folding. The popularity of FFS is not only due to its ease of implementation, but also because it is not very sensitive to the particular choice of an order parameter. The order parameter utilized in conventional FFS, however, still needs to satisfy a stringent smoothness criterion in order to assure sequential crossing of FFS milestones. This condition is usually violated for order parameters utilized for describing aggregation phenomena such as crystallization. Here, we present a generalized FFS algorithm for which this smoothness criterion is no longer necessary, and apply it to compute homogeneous crystal nucleation rates in several systems. Our numerical tests reveal that conventional FFS can sometimes underestimate the nucleation rate by several orders of magnitude.