Treffer: PyVisA: Visualization and Analysis of path sampling trajectories.

Title:
PyVisA: Visualization and Analysis of path sampling trajectories.
Source:
Journal of Computational Chemistry; 3/5/2021, Vol. 42 Issue 6, p435-446, 12p
Database:
Complementary Index

Weitere Informationen

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). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computational Chemistry is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)