Simulating rare events using a Weighted Ensemble-based string method

TL;DR

This paper presents a novel extension of the Weighted Ensemble path sampling method, integrating a string approach to efficiently sample rare events along reaction pathways in high-dimensional systems, applicable to both equilibrium and non-equilibrium scenarios.

Contribution

The authors develop a new method combining Weighted Ensemble sampling with a finite-temperature string approach, enabling efficient pathway-focused sampling in complex systems.

Findings

Efficient sampling of rare events in high-dimensional systems.

Comparison shows improved efficiency over brute force simulations.

Application to protein conformational change demonstrates practical utility.

Abstract

We introduce an extension to the Weighted Ensemble (WE) path sampling method to restrict sampling to a one dimensional path through a high dimensional phase space. Our method, which is based on the finite-temperature string method, permits efficient sampling of both equilibrium and non-equilibrium systems. Sampling obtained from the WE method guides the adaptive refinement of a Voronoi tessellation of order parameter space, whose generating points, upon convergence, coincide with the principle reaction pathway. We demonstrate the application of this method to several simple, two-dimensional models of driven Brownian motion and to the conformational change of the nitrogen regulatory protein C receiver domain using an elastic network model. The simplicity of the two-dimensional models allows us to directly compare the efficiency of the WE method to conventional brute force simulations and other path sampling algorithms, while the example of protein conformational change demonstrates how the method can be used to efficiently study transitions in the space of many collective variables.