What is WESTPA?

WESTPA (The Weighted Ensemble Simulation Toolkit with Parallelization and Analysis) is a high-performance Python framework for applying the weighted ensemble (WE) path sampling strategy, which enables simulations of processes that are orders of magnitude longer than the simulations themselves. See Overview of WE simulations. The WESTPA software package includes (1) binned/binless WE strategies, including the Huber & Kim algorithm, (2) options for reweighting to a steady state to estimate rates and/or state populations from shorter trajectories, and (3) plugins/extensions for advanced customisation.

Key features of WESTPA:

  • Highly scalable. Scales out to thousands of CPU cores or GPUs.
  • Interoperable. Designed to conveniently interface with any stochastic dynamics engine (e.g., molecular dynamics, Monte Carlo).
  • Extensible. Our modular design makes it straightforward to develop plug-ins.
  • Portable. The software can be used with any Unix operating system (e.g. Linux, OS X), including typical clusters and supercomputers.
  • Free and open source. All source code is available under the MIT license.

WESTPA plugins/extensions:

  • A WE-based string method. [src] [pdf]
  • Markovian WE Milestoning (M-WEM). [src] [pdf]
  • A hybrid Gaussian-accelerated MD and WE method. [src] [pdf]
  • DeepWEST: Deep-learned kinetic modeling for generating initial states. [src] [pdf]
  • WE rule-based modeling (WEBNG) for systems biology. [src] [pdf]

Please cite us when using WESTPA:

MC Zwier, JL Adelman, JW Kaus, AJ Pratt, KF Wong, NB Rego, E Suárez, S Lettieri, DW Wang, M Grabe, DM Zuckerman, and LT Chong. “WESTPA: An interoperable, highly scalable software package for weighted ensemble simulation and analysis”. J. Chem. Theory Comput., 11: 800-809 (2015).

JD Russo*, S Zhang*, JMG Leung*, AT Bogetti*, JP Thompson, AJ DeGrave, PA Torrillo, AJ Pratt, KF Wong, J Xia, J Copperman, JL Adelman, MC Zwier, DN LeBard, DM Zuckerman, and LT Chong. "WESTPA 2.0: High-Performance Upgrades for Weighted Ensemble Simulations and Analysis of Longer-Timescale Applications". J. Chem. Theory Comput., 18 (2), 638–649 (2022). *equal authorship

Obtaining and Installing WESTPA

Follow our Getting Started guide or Installing WESTPA page.

Documentation and Tutorials

To leverage the full power of the WE strategy, it is essential to invest the time to learn the theory and best practices for running the simulations.

For documentation, please see the WESTPA Wiki (general usage) and Sphinx documentation (code).

Our wiki page with WESTPA tutorials and best practices can be found here.

We suggest subscribing to the WESTPA user mailing list. Before posting an issue to the list, please use the most recent version of WESTPA and search the archives.

If you have completed the LiveCoMS WESTPA tutorials and are ready for production simulations of your system, we invite you to join us via zoom for a WE data club where we provide "no strings attached" expert advice on getting your simulations off the ground.

For this data club, please be ready to present preliminary analysis of your WESTPA simulation(s) with w_pdist and plothist to provide plots of the probability distribution as a function of your chosen progress coordinate, time-evolution of this probability distribution, and the average probability distribution as a function of multiple dimensions. More detailed instructions can be found in the LiveCoMS WESTPA tutorials.

To make a feature request, please fill out this form

WESTPA Contributions and Governance

We welcome contributions to WESTPA code and/or documentation! Please see our Governance Document for further details.

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