Using stochastic models calibrated from nanosecond nonequilibrium simulations to approximate mesoscale information

Area of Science:

• Computational Chemistry
• Statistical Mechanics
• Molecular Dynamics

Background:

• Calculating free energy landscapes and transport properties in complex systems is computationally demanding.
• Traditional methods often require extensive simulations or simplified models.
• Accurate estimation of potential of mean force and diffusion coefficients is crucial for understanding molecular processes.

Purpose of the Study:

• To demonstrate the utility of the surrogate process approximation (SPA) method for computing key thermodynamic and kinetic properties.
• To show that SPA can accurately estimate potential of mean force and diffusion coefficients using a minimal number of nonequilibrium trajectories.
• To provide robust confidence bands accounting for system variability and thermal fluctuations.

Main Methods:

• Utilized bidirectional nonequilibrium trajectories from complex systems.
• Applied maximum-likelihood methods to estimate stochastic differential equations (SDEs) approximating system dynamics.
• Generated a collection of SPA models from individual time series for comprehensive analysis.

Main Results:

• Successfully computed potential of mean force and diffusion coefficients with high accuracy using only 10-20 trajectories.
• Developed confidence bands that rigorously incorporate system initial configurations, path variability, and thermal noise.
• Demonstrated the methodology's effectiveness through molecular dynamics simulations of potassium ion transport in gramicidin A channels.

Conclusions:

• The surrogate process approximation (SPA) method offers an efficient and reliable approach for analyzing complex molecular dynamics.
• SPA provides valuable insights into system thermodynamics and kinetics, even with limited simulation data.
• The method's applicability extends to analyzing experimental single-molecule time series, broadening its impact.