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We developed a new metric to measure noise correlation time in molecular motion. This method helps understand complex diffusion by analyzing velocity data, revealing the role of long-range correlations.

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Area of Science:

  • Physical Chemistry
  • Statistical Mechanics
  • Computational Physics

Background:

  • The generalized Langevin equation models molecular motion with memory and noise.
  • Memory effects in noise significantly impact molecular diffusion.
  • Estimating noise correlation time is difficult due to challenges in measuring memory terms.

Purpose of the Study:

  • To propose a novel metric for probing noise correlation time.
  • To investigate the influence of memory effects on molecular diffusion dynamics.
  • To apply the metric to complex diffusive phenomena.

Main Methods:

  • Developed a metric based on the deviation between characteristic times of configurational diffusion and kinetic energy diffusion.
  • Utilized the velocity autocorrelation function for metric calculation.
  • Validated the metric using analytical and numerical results from various physical models.

Main Results:

  • The proposed metric effectively probes noise correlation time.
  • Memory effects were shown to delay relaxation times between displacement and velocity response functions.
  • Long-range noise correlations were identified as crucial in non-exponential relaxation and anomalous diffusion.

Conclusions:

  • The new metric provides a practical approach to quantify noise correlation time.
  • This method aids in understanding complex molecular diffusion in systems like molecular hydrodynamics and crowded environments.
  • The findings highlight the significant role of noise correlations in non-trivial diffusive behaviors.