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We developed a new method using stochastic resetting to infer long-timescale kinetics from molecular dynamics simulations. This approach accurately estimates non-exponential processes, overcoming limitations of standard simulations and enhancing sampling efficiency.

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Area of Science:

  • Computational Chemistry
  • Chemical Kinetics
  • Molecular Dynamics

Background:

  • Standard molecular dynamics simulations are limited to short timescales (∼1μs).
  • Enhanced sampling methods and inference schemes are needed for slower processes.
  • Existing inference schemes often assume exponential kinetics, unsuitable for non-exponential processes like power-law decay.

Purpose of the Study:

  • To develop a novel inference scheme for long-timescale, non-exponential kinetics.
  • To utilize molecular dynamics simulations accelerated by stochastic resetting.
  • To overcome the limitations of current kinetics inference methods.

Main Methods:

  • Simulations enhanced by stochastic resetting were employed.
  • The scheme infers kinetics from the first-passage time distribution.
  • It leverages short-timescale sampling and long-time asymptotic estimation.

Main Results:

  • Stochastic resetting effectively samples the first-passage time distribution.
  • The method successfully estimates long-time asymptotics for kinetics inference.
  • Applied to a model system and a peptide, it achieved over an order of magnitude acceleration.
  • Unbiased mean first-passage times were accurately estimated.

Conclusions:

  • The proposed inference scheme accurately captures non-exponential kinetics.
  • Stochastic resetting accelerates sampling and enables reliable kinetics estimation.
  • This method advances the study of slow chemical processes using molecular dynamics.