Automatic, optimized interface placement in forward flux sampling simulations.

Area of Science:

• Computational physics
• Statistical mechanics
• Rare event simulation

Background:

• Forward flux sampling (FFS) is an efficient method for simulating rare events in complex systems.
• The performance of FFS is highly dependent on the strategic placement of interfaces in phase space.
• Manual interface placement can be time-consuming and requires prior system knowledge.

Purpose of the Study:

• To develop and present two novel, automated methods for adaptive interface placement in FFS simulations.
• To enable FFS simulations to efficiently navigate phase space bottlenecks without user intervention.
• To improve the overall performance and accessibility of FFS for rare event simulations.

Main Methods:

• Developed two on-the-fly algorithms for adaptive interface placement during FFS simulations.
• Interfaces are automatically positioned to optimize advancement through phase space bottlenecks.
• Methods require no prior knowledge of system dynamics or bottleneck locations.

Main Results:

• Demonstrated improved FFS efficiency in both a single-particle test case and Yukawa particle crystallization.
• Successfully simulated rare events involving complex phase space trajectories with multiple bottlenecks.
• Validated the effectiveness of automated, adaptive interface placement in enhancing simulation performance.

Conclusions:

• The presented methods significantly facilitate FFS setup by eliminating manual interface optimization.
• Automated adaptive interface placement improves FFS efficiency, particularly for complex rare events.
• These advancements make FFS more accessible and powerful for studying challenging phenomena in physics and chemistry.