PyVisA: Visualization and Analysis of path sampling trajectories

Rare event methods applied to molecular simulations are growing in popularity, accessible and customizable software solutions have thus been developed and released. One of the most recent is PyRETIS, an open Python library for performing path sampling simulations. Here, we introduce PyVisA, a postprocessing package for path sampling simulations, which includes visualization and analysis tools for interpreting path sampling outputs. PyVisA integrates PyRETIS functionalities and aims to facilitate the determination of: (a) the correlation of the order parameter with other descriptors; (b) the presence of latent variables; and (c) intermediate meta-stable states. To illustrate some of the main PyVisA features, we investigate the proton transfer reaction in a protonated water trimer simulated via a simple polarizable model (Stillinger-David).

Automated summary

Rare event methods in molecular simulations are gaining popularity, leading to the development of accessible and customizable software solutions. PyRETIS is an open Python library for path sampling simulations, while PyVisA is a postprocessing package that includes visualization and analysis tools to interpret path sampling outputs. PyVisA aims to facilitate the determination of correlation between the order parameter and other descriptors, the presence of latent variables, and intermediate meta-stable states.