WESTPA 2.0: High-performance upgrades for weighted ensemble simulations and analysis of longer-timescale applications

The weighted ensemble (WE) family of methods is one of several statistical-mechanics based path sampling strategies that can provide estimates of key observables (rate constants, pathways) using a fraction of the time required by direct simulation methods such as molecular dynamics or discrete-state stochastic algorithms. WE methods oversee numerous parallel trajectories using intermittent overhead operations at fixed time intervals, enabling facile interoperability with any dynamics engine. Here, we report on major upgrades to the WESTPA software package, an open-source, high-performance framework that implements both basic and recently developed WE methods. These upgrades offer substantial improvements over traditional WE. Key features of the new WESTPA 2.0 software enhance efficiency and ease of use: an adaptive binning scheme for more efficient surmounting of large free energy barriers, streamlined handling of large simulation datasets, exponentially improved analysis of kinetics, and developer-friendly tools for creating new WE methods, including a Python API and resampler module for implementing both binned and “binless” WE strategies. Table of Contents/Abstract Image For the manuscript “WESTPA 2.0: High-performance upgrades for weighted ensemble simulations and analysis of longer-timescale applications” by Russo et al . ![Figure][1] ### Competing Interest Statement The authors declare the following competing financial interest(s). L.T.C. is a current member of the Scientific Advisory Board of OpenEye Scientific and an Open Science Fellow with Roivant Sciences. S.Z., J.P.T., J.X., and D.N.L. are employees of OpenEye Scientific. [1]: pending:yes