PyVisA: Visualization and Analysis of path sampling trajectories

Abstract

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).

Exploring the effects of approach velocity on depletion force and coalescence in oil-in-water emulsions

“…Additionally, the combined efforts of experimental measurements detailed in this paper and simulations will be important for further progress in the understanding of these emulsions. Ongoing experimental studies are therefore in our group complemented by the use of rare event simulations using the PyRETIS 62,63 and PyVisA 64 packages to aid in understanding the dynamics of thin film breakage.…”

Permeation Rates of Oxygen through a Lipid Bilayer Using Replica Exchange Transition Interface Sampling

“…For O 2 this has been found to be a valid assumption in previous work. , No statistically relevant correlation between the distance of the atoms from the center of the POPC membrane has been observed. For the analysis, the correlation matrix based on Pearson’s correlation coefficients and PyVisA has been used . The unconditional flux f (1) for a single permeant can be expressed as Here, the subindex (1) refers to the use of a single permeant and Δ t is the time interval at which the order parameter is measured.…”

Chemistrees: Data-Driven Identification of Reaction Pathways via Machine Learning

“…Our method can be further expanded by considering a higher number of descriptors alongside the distance matrix. Velocities, angles between molecules, coarse-graining procedures, or a mix of user-defined functions 59 could be fed into the DT and subsequent analysis.…”