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PyVisA : Visualization and Analysis of path sampling trajectories
Author(s) -
Aarøen Ola,
Kiær Henrik,
Riccardi Enrico
Publication year - 2021
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.26467
Subject(s) - computer science , python (programming language) , visualization , sampling (signal processing) , trimer , path (computing) , software , molecular dynamics , data mining , computational science , algorithm , theoretical computer science , computational chemistry , chemistry , programming language , detector , telecommunications , dimer , organic chemistry
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).