PyRETIS 3: Conquering rare and slow events without boundaries

W Vervust and DT Zhang and A Ghysels and S Roet and TS van Erp and E Riccardi, JOURNAL OF COMPUTATIONAL CHEMISTRY, 45, 1224-1234 (2024).

DOI: 10.1002/jcc.27319

We present and discuss the advancements made in PyRETIS 3, the third instalment of our Python library for an efficient and user-friendly rare event simulation, focused to execute molecular simulations with replica exchange transition interface sampling (RETIS) and its variations. Apart from a general rewiring of the internal code towards a more modular structure, several recently developed sampling strategies have been implemented. These include recently developed Monte Carlo moves to increase path decorrelation and convergence rate, and new ensemble definitions to handle the challenges of long-lived metastable states and transitions with unbounded reactant and product states. Additionally, the post-analysis software PyVisa is now embedded in the main code, allowing fast use of machine-learning algorithms for clustering and visualising collective variables in the simulation data. PyRETIS 3 is the latest update for the software PyRETIS, a python library to optimize the sampling of rare events at the molecular level. The update introduces modular code for flexibility and implements innovative sampling strategies, including advanced Monte Carlo moves. It tackles challenges of metastable states with new ensemble definitions and integrates PyVisa for efficient post-analysis using machine learning algorithms. These enhancements promise greater accessibility and deeper insights into molecular systems. image

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