Steady-state simulations using weighted ensemble path sampling

TL;DR

This paper advances weighted ensemble path sampling methods to efficiently achieve steady-state sampling in complex systems with metastable states, demonstrating improved speed and accuracy over brute-force approaches.

Contribution

The authors develop an enhanced weighted ensemble scheme that accelerates steady-state attainment in complex systems, building on existing methods and applicable to equilibrium sampling.

Findings

Enhanced WE scheme is significantly faster than brute-force methods.

Both WE approaches are validated on model systems confirming correctness.

The methods are effective for systems with reaction coordinates accurately reflected by WE bins.

Abstract

We extend the weighted ensemble (WE) path sampling method to perform rigorous statistical sampling for systems at steady state. The straightforward steady-state implementation of WE is directly practical for simple landscapes, but not when significant metastable intermediates states are present. We therefore develop an enhanced WE scheme, building on existing ideas, which accelerates attainment of steady state in complex systems. We apply both WE approaches to several model systems confirming their correctness and efficiency by comparison with brute-force results. The enhanced version is significantly faster than the brute force and straightforward WE for systems with WE bins that accurately reflect the reaction coordinate(s). The new WE methods can also be applied to equilibrium sampling, since equilibrium is a steady state.