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Rare switching events in non-stationary systems
Nils B Becker1, Pieter Rein ten Wolde
1FOM Institute for Atomic and Molecular Physics (AMOLF), Science Park 104, 1098 XG Amsterdam, The Netherlands.
The Journal of Chemical Physics
|May 16, 2012
Summary
This study extends coarse-grained models for complex physical systems. It introduces methods to accurately describe transitions in non-stationary systems with memory, generalizing Markov state models.
Area of Science:
- Statistical Mechanics
- Physical Chemistry
- Computational Physics
Background:
- Complex physical systems are often modeled as transitions between metastable states.
- For time-homogeneous systems, these transitions are characterized by rate constants.
- Existing models struggle with non-stationary systems and those with finite memory.
Purpose of the Study:
- To extend coarse-grained descriptions of physical systems to non-stationary conditions and finite memory.
- To identify conditions where time-dependent rates are physically meaningful.
- To provide methods for measuring time-dependent and history-dependent rates in simulations.
Main Methods:
- Developing theoretical frameworks for non-stationary systems.
- Deriving microscopic expressions for rate constants.
- Analyzing systems with finite memory effects.
Main Results:
- Identified physical regimes where time-dependent rates are meaningful.
- Provided microscopic expressions for measuring time-dependent and history-dependent rates.
- Established a foundation for generalizing Markov state models.
Conclusions:
- The proposed framework allows for accurate coarse-grained descriptions of complex systems beyond traditional limitations.
- This work enables the generalization of Markov state models to time-dependent and non-Markovian systems.
- The developed methods are applicable to microscopic simulations for enhanced accuracy.
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