Contour forward flux sampling: Sampling rare events along multiple collective variables.

Area of Science:

• Computational Chemistry and Physics
• Statistical Mechanics
• Biomolecular Simulations

Background:

• Rare event transitions in complex systems often depend on multiple collective variables (CVs).
• Identifying the optimal set of CVs for studying these events is challenging and typically requires prior knowledge of the system's energy landscape.
• Existing methods may struggle with systems where the relevant CVs are unknown or change dynamically.

Purpose of the Study:

• To develop a novel computational method for studying rare event transitions involving multiple collective variables (CVs) simultaneously.
• To enable the analysis of rare events without requiring prior knowledge of the system's energy landscape or the appropriate combination of CVs.
• To provide a more efficient and adaptable approach for exploring complex conformational changes and reaction pathways.

Main Methods:

• Introduction of contour forward flux sampling (cFFS), a new simulation technique.
• cFFS dynamically places nonlinear interfaces based on the collective progress of simulations.
• The method operates without a priori information about the energy landscape or relevant CVs.

Main Results:

• Demonstrated the efficacy of cFFS on analytical potential energy surfaces.
• Successfully applied cFFS to study a complex conformational change in alanine dipeptide.
• The results showcase cFFS's ability to handle multiple CVs adaptively and efficiently.

Conclusions:

• Contour forward flux sampling (cFFS) is a powerful new method for the investigation of rare event transitions.
• cFFS overcomes limitations of previous methods by not requiring prior knowledge of collective variables or energy landscapes.
• This approach offers a significant advancement for computational studies of complex molecular dynamics and chemical processes.