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Understanding and controlling regime switching in molecular diffusion.

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Researchers used statistical inference to understand how molecular diffusion is affected by long jumps and sticks. They found specific molecular movements predict these diffusion behaviors, enabling control over diffusion in simulations.

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Area of Science:

  • * Physical Chemistry
  • * Computational Chemistry
  • * Statistical Mechanics

Background:

  • * Molecular diffusion is crucial in many chemical and physical processes.
  • * Diffusion dynamics are often influenced by complex trajectories like long jumps and sticking events.
  • * Understanding these dynamics is key to controlling molecular motion.

Purpose of the Study:

  • * To investigate the origins of long jumps and sticks in molecular diffusion.
  • * To establish a predictive link between molecular internal dynamics and diffusion behavior.
  • * To demonstrate the feasibility of controlling diffusion via external influence on molecular dynamics.

Main Methods:

  • * Employed statistical inference techniques, inspired by Granger causality.
  • * Utilized molecular-dynamics simulations of a benzene molecule on a graphite substrate.
  • * Analyzed internal degrees of freedom as predictor variables for diffusion events.

Main Results:

  • * Identified specific internal molecular fluctuations correlating with long jumps and sticks.
  • * Demonstrated that altering these predictor fluctuations can control molecular diffusion.
  • * Confirmed the generic applicability of the method to complex systems.

Conclusions:

  • * Statistical inference and data-mining can reveal phase-space structures in dynamical systems.
  • * Molecular internal dynamics can be leveraged to control diffusion.
  • * The presented approach offers a pathway for experimental control of diffusion in various applications.