Kinetic Network in Milestoning: Clustering, Reduction, and Transition Path Analysis.

Area of Science:

• Computational Chemistry
• Statistical Mechanics
• Chemical Kinetics

Background:

• Milestoning is a powerful method for analyzing complex chemical systems.
• High-dimensional Milestoning networks can be computationally intensive and difficult to interpret.
• Extracting essential kinetic information from these networks is crucial for understanding molecular dynamics.

Purpose of the Study:

• To present a novel algorithm for reducing high-dimensional Milestoning networks.
• To preserve key kinetic properties like residence and passage times during reduction.
• To enable efficient analysis of transition pathways in complex systems.

Main Methods:

• The reduction of the Milestoning (ReM) algorithm involves three main steps.
• Nodes (milestones) are clustered based on metastability using an auxiliary continuous-time Markov chain.
• A reduced network is formed by transforming the clustered network, followed by transition pathway analysis using transition path theory.

Main Results:

• The ReM algorithm successfully reduces the dimensionality of Milestoning networks.
• Essential kinetic information, including local residence time, exit time, and mean first passage time, is preserved.
• The method was validated on a toy model and a solvated alanine dipeptide system.

Conclusions:

• The ReM algorithm offers an efficient approach to analyze high-dimensional Milestoning kinetic networks.
• This reduction technique simplifies complex systems while retaining critical kinetic insights.
• The method is applicable to both time-reversible and non-time-reversible networks from simulations.