Automatic, optimized interface placement in forward flux sampling simulations

TL;DR

This paper introduces two adaptive, automatic interface placement methods for forward flux sampling simulations, enhancing efficiency and ease of setup in rare event simulations without prior knowledge.

Contribution

The paper presents novel on-the-fly adaptive algorithms for optimal interface placement in FFS, reducing manual intervention and improving simulation performance.

Findings

Methods successfully applied to test problems and Yukawa particle crystallization.

Adaptive placement improves efficiency by focusing interfaces at bottlenecks.

Automation reduces setup time and user bias in FFS simulations.

Abstract

Forward flux sampling (FFS) provides a convenient and efficient way to simulate rare events in equilibrium or non-equilibrium systems. FFS ratchets the system from an initial state to a final state via a series of interfaces in phase space. The efficiency of FFS depends sensitively on the positions of the interfaces. We present two alternative methods for placing interfaces automatically and adaptively in their optimal locations, on-the-fly as an FFS simulation progresses, without prior knowledge or user intervention. These methods allow the FFS simulation to advance efficiently through bottlenecks in phase space by placing more interfaces where the probability of advancement is lower. The methods are demonstrated both for a single-particle test problem and for the crystallization of Yukawa particles. By removing the need for manual interface placement, our methods both facilitate the setting up of FFS simulations and improve their performance, especially for rare events which involve complex trajectories through phase space, with many bottlenecks.