Transition path sampling for non-equilibrium dynamics without predefined reaction coordinates.

Area of Science:

• Statistical Mechanics
• Computational Chemistry
• Complex Systems

Background:

• Rare events in complex systems are challenging to study using traditional simulation methods.
• Non-equilibrium dynamics often exhibit complex transition pathways that are difficult to capture.
• Existing methods like transition path sampling (TPS) often require predefined reaction coordinates.

Purpose of the Study:

• To develop novel algorithms for efficient harvesting of rare event trajectories.
• To enable the study of rare events without relying on predefined reaction coordinates.
• To bridge the gap between transition path sampling and forward flux sampling methodologies.

Main Methods:

• Development of two novel transition path sampling (TPS) algorithms.
• Utilizing non-equilibrium dynamics for trajectory generation.
• Implementing Monte Carlo random walks in trajectory space.
• Instantaneous reaction coordinate based on the current path.

Main Results:

• Successfully harvested ensembles of rare event trajectories.
• Demonstrated applicability on toy models with equilibrium and non-equilibrium dynamics.
• Applied to an active Brownian particle system, revealing transition mechanisms.
• Identified transitions between steady states occurring via locally ordered, globally disordered states.

Conclusions:

• The novel TPS algorithms offer a powerful approach for studying rare events in complex systems.
• Eliminating the need for predefined reaction coordinates enhances the flexibility and applicability of the methods.
• The findings provide insights into the nature of transitions in active matter systems.