PyVisA

PyVisA provides visualization and analysis for postprocessing path sampling simulations generated by PyRETIS to interpret rare event dynamics in molecular dynamics studies.

Key Features:

• Postprocessing of path sampling simulations: Processes trajectory and path data produced by path sampling simulations for downstream analysis.
• Visualization and analysis tools: Generates visualizations and quantitative analyses to interpret path sampling outputs.
• Integration with PyRETIS: Operates on outputs from the PyRETIS open-source Python library for path sampling simulations.
• Correlation analysis: Evaluates correlations between order parameters and other descriptors within simulation data.
• Latent variable detection: Identifies latent variables that influence molecular system behavior in path ensembles.
• Identification of meta-stable states: Detects intermediate meta-stable states present in sampled transition pathways.

Scientific Applications:

• Analysis of path sampling simulations: Interprets rare event trajectories and path ensembles from molecular dynamics path sampling.
• Correlation studies: Explores relationships between order parameters and other molecular descriptors to elucidate reaction coordinates.
• Latent variable discovery: Reveals hidden degrees of freedom that affect transition mechanisms in molecular systems.
• Meta-stable state characterization: Identifies and characterizes intermediate meta-stable states along transition pathways.
• Proton transfer case study: Applied to the proton transfer reaction in a protonated water trimer using the Stillinger-David polarizable model.

Methodology:

Postprocessing of path sampling simulations from PyRETIS using visualization and analysis routines, correlation analysis between order parameters and descriptors, latent variable detection, and identification of meta-stable states.