PyVisA: Visualization and Analysis of path sampling trajectories.

Area of Science:

• Computational chemistry
• Molecular dynamics simulations
• Software development

Background:

• Rare event methods in molecular simulations are increasingly utilized.
• Accessible software solutions like PyRETIS have been developed for path sampling.
• Postprocessing tools are crucial for interpreting complex simulation data.

Purpose of the Study:

• Introduce PyVisA, a postprocessing package for path sampling simulations.
• Provide visualization and analysis tools for path sampling outputs.
• Facilitate the determination of order parameter correlations, latent variables, and meta-stable states.

Main Methods:

• Developed PyVisA, a Python package integrating with PyRETIS.
• Utilized PyVisA for postprocessing path sampling simulation data.
• Investigated proton transfer in a protonated water trimer using a polarizable model.

Main Results:

• Demonstrated PyVisA's capability to analyze and visualize path sampling outputs.
• Illustrated the correlation of order parameters with other descriptors.
• Showcased the identification of latent variables and intermediate meta-stable states.

Conclusions:

• PyVisA enhances the interpretation of rare event molecular simulations.
• The package facilitates a deeper understanding of complex molecular reactions.
• PyVisA serves as a valuable tool for researchers in computational chemistry.