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Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
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Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
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The generation of electrical current in semiconductors is fundamentally driven by two mechanisms: drift and diffusion. These processes are essential for the functionality and performance of semiconductor-based devices.
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The motion of molecules in a gas is random in magnitude and direction for individual molecules, but a gas of many molecules has a predictable distribution of molecular speeds. This predictable distribution of molecular speeds is known as the Maxwell-Boltzmann distribution. The distribution of molecular speeds in liquids is comparable to that of gases but not identical and can help to understand the phenomenon of the boiling and vapor pressure of a liquid. Consider that a molecule requires a...
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Calculating Position-Dependent Diffusivity in Biased Molecular Dynamics Simulations.

Jeffrey Comer1,2, Christophe Chipot3,4, Fernando D González-Nilo1,2,5

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Summary
This summary is machine-generated.

Calculating position-dependent diffusivity in molecular simulations is crucial for kinetic studies. This new Bayesian inference method accurately computes diffusivity even with complex biasing forces, improving simulation analysis.

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Area of Science:

  • Computational Chemistry
  • Statistical Mechanics
  • Molecular Dynamics

Background:

  • Accurate kinetic quantities from molecular simulations require free energy and position-dependent diffusivity.
  • Calculating diffusivity is challenging due to biasing forces in common free-energy mapping methods.

Purpose of the Study:

  • To develop and validate a method for calculating position-dependent diffusivities in molecular simulations with time-dependent biasing forces.
  • To address limitations of existing methods in accurately determining diffusivity under complex simulation conditions.

Main Methods:

  • Utilized a Bayesian inference scheme to analyze simulations with known time-dependent biasing forces.
  • Applied the method to an explicitly diffusive model and equilibrium molecular dynamics simulations of liquid water.
  • Compared results with an established method and tested on systems with challenging free-energy landscapes.

Main Results:

  • The method successfully calculates position-dependent diffusivities in simulations with biasing forces.
  • Consistent diffusivity results were obtained even in short simulations with partially converged adaptive biasing forces.
  • Performance was validated against an established method in liquid water simulations.

Conclusions:

  • The developed Bayesian inference method provides a robust way to compute position-dependent diffusivities.
  • This approach enhances the analysis of molecular dynamics simulations, particularly those employing biasing techniques.
  • It offers a reliable tool for studying kinetic properties in complex systems where equilibrium sampling is difficult.