Adaptive single replica multiple state transition interface sampling.

Area of Science:

• Computational Chemistry
• Statistical Mechanics
• Molecular Dynamics

Background:

• Multiple state transition path sampling methods are crucial for studying rare events between metastable states.
• Existing methods struggle with switching between different pathways and often require numerous replicas.

Purpose of the Study:

• To develop a more efficient algorithm for sampling rare transitions between multiple metastable states.
• To overcome the limitations of existing path sampling techniques, particularly the difficulty in switching between qualitatively different pathways.

Main Methods:

• Introduced a single replica sampling algorithm that samples one interface at a time.
• Employed a Wang-Landau approach or fixed bias for efficient exploration of path space.
• Illustrated the method on model systems including diffusion, Lennard Jones cluster isomerization, and alanine dipeptide isomerization in explicit water.

Main Results:

• The new algorithm efficiently samples rare transitions between multiple metastable states.
• Successfully demonstrated the method's applicability across diverse model systems.
• Overcame the drawbacks of previous methods, enabling efficient pathway exploration with a single replica.

Conclusions:

• The developed single replica sampling algorithm offers a significant advancement in exploring complex reaction pathways.
• This method provides a more efficient and versatile tool for computational studies of rare events.
• The approach is broadly applicable to various systems in computational chemistry and physics.