Stochastic Resetting for Enhanced Sampling.

Area of Science:

• Computational chemistry
• Statistical mechanics
• Biophysics

Background:

• Molecular dynamics simulations are essential for studying complex phenomena.
• Many crucial processes, such as protein folding, occur over extremely long timescales.
• Standard simulations struggle to capture these rare, slow events.

Purpose of the Study:

• To introduce and validate stochastic resetting as a method for enhanced sampling in molecular dynamics.
• To demonstrate the acceleration of long timescale processes in molecular simulations.
• To show that stochastic resetting can accurately determine mean transition times that are otherwise inaccessible.

Main Methods:

• Implementing stochastic resetting in molecular dynamics simulations.
• Applying the method to various systems, from simple models to complex molecular systems.
• Analyzing simulation data to quantify acceleration and recover mean transition times.

Main Results:

• Stochastic resetting accelerated simulations of long timescale processes by up to an order of magnitude.
• The method successfully recovered mean transition times from accelerated simulations.
• The effectiveness was demonstrated across diverse model and molecular systems.

Conclusions:

• Stochastic resetting is a powerful technique for enhanced sampling in molecular dynamics.
• It significantly speeds up the observation of rare, slow events.
• This method offers a reliable way to determine otherwise intractable timescales, aiding in the study of complex phenomena.