Sampling rare events in nonequilibrium and nonstationary systems

Area of Science:

• Computational physics
• Statistical mechanics
• Chemical kinetics

Background:

• Rare event sampling is crucial for understanding complex systems.
• Existing methods are often impractical for nonequilibrium conditions.
• Calculating probabilities away from equilibrium is computationally challenging.

Purpose of the Study:

• To present a novel computational method for rare event sampling.
• To enable calculations under general nonequilibrium conditions.
• To determine the time-series of rare event probabilities.

Main Methods:

• Phase space subdivision for system analysis.
• Validation using diverse models: Glauber-Ising, Kawasaki-Ising, and asymmetric exclusion process.
• Benchmarking for scalability with increasing phase space subdivisions.

Main Results:

• The novel method successfully calculates rare event probabilities over time.
• The approach is validated across multiple complex dynamic systems.
• The method demonstrates good scalability with finer phase space subdivisions.

Conclusions:

• The developed method is effective for rare event sampling in nonequilibrium systems.
• It offers a practical approach without needing prior knowledge of reaction pathways.
• This work advances computational capabilities for studying dynamic processes.