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.
This project contains a number of examples illustrating the use of a Weighted Ensemble-based string method
Python implementation of the Weighted Ensemble-based string method that provides an efficient algorithm for sampling equilibrium and non-equilibrium transitions in complex systems.
Python implementations of several example systems used to demonstrate a Weighted Ensemble-based string method.
An up-to-date version of the code can be found at:
with corresponding documentation at: