Diffusion: Difference between revisions

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The '''diffusion coefficient''', is given by
'''Diffusion''' is the process behind [[Brownian motion]]. It was described
by [[Albert Einstein]] in one of his ''annus mirabilis'' papers of 1905.
What follows applies to homogeneous systems, see [[diffusion at interfaces]]
for a non-homogeneous case.


:<math>D = \frac{1}{3} \int_0^\infty \langle v_i(t) \cdot v_i(0)\rangle ~dt</math>
The diffusion equation that describes this process is
:<math>\frac{\partial P(r,t)}{\partial t}= D \nabla^2 P(z,t),</math>
where <math>D</math> is the (self-)'''diffusion coefficient'''.
For initial conditions for a [[Dirac delta distribution |Dirac delta function]] at the origin, and
boundary conditions that force the vanishing of <math>P(r,t)</math>
and its gradient at large distances, the solution factorizes as <math>P(r,t)=P(x,t)P(y,t)P(z,t)</math>,
with a spreading [[Gaussian distribution |Gaussian]] for each of the Cartesian components:
:<math> P(x,t)=\frac{1}{\sqrt{4\pi D t}} \exp
  \left[ - \frac{x^2}{4 D t} \right]. </math>
 
==Einstein relation==


where <math>v_i(t)</math> is the center of mass velovity of molecule <math>i</math>.
It follows from the previous equation that, for each of the Cartesian components, e.g. <math>x</math>:
:<math>D = \lim_{t \rightarrow \infty} \frac{1}{2t} \langle \vert x_i(t) - x_i(0) \vert^2\rangle  </math>,
for every particle <math>i</math>. Therefore, an average over all particles can be employed in
order to improve statistics. The same applies to time averaging: in equilibrium the average
from <math>0</math> to <math>t</math> must equal the average from <math>\tau</math> to <math>t+\tau</math>,
so several time segments from the same simulation may be averaged for a given interval [2].
Adding all components, the following also applies:
:<math>D = \lim_{t \rightarrow \infty} \frac{1}{6t } \langle \vert \mathbf{r}_i(t) - \mathbf{r}_i(0) \vert^2\rangle  </math>


==Einstein relation==
==Green-Kubo relation==
:''Main article: [[Green-Kubo relations]]''
:<math>D = \frac{1}{3} \int_0^\infty \langle v_i(t) \cdot v_i(0)\rangle ~dt</math>


:<math>2tD = \frac{1}{3} \langle \vert r_i(t) \cdot r_i(0) \vert^2\rangle  </math>
where <math>v_i(t)</math> is the center of mass velocity of molecule <math>i</math>. Note
that this connect the diffusion coefficient with the velocity [[autocorrelation]].
==See also==
*[[Rotational diffusion]]
==References==
==References==
#[http://dx.doi.org/10.1080/00268970701348758 G. L. Aranovich and M. D. Donohue "Limitations and generalizations of the classical phenomenological model for diffusion in fluids", Molecular Physics '''105''' 1085-1093 (2007)]
<references/>
;Related reading
*[http://books.google.es/books?id=XmyO2oRUg0cC&dq=understanding+molecular+simulations&psp=1 Daan Frenkel and Berend Smit "Understanding Molecular Simulation: From Algorithms to Applications". Academic Press 2002]
*[http://dx.doi.org/10.1063/1.1786579 Karsten Meier, Arno Laesecke, and Stephan Kabelac "Transport coefficients of the Lennard-Jones model fluid. II Self-diffusion" J. Chem. Phys. '''121''' pp. 9526-9535 (2004)]
*[http://dx.doi.org/10.1080/00268970701348758 G. L. Aranovich and M. D. Donohue "Limitations and generalizations of the classical phenomenological model for diffusion in fluids", Molecular Physics '''105''' 1085-1093 (2007)]
*[http://dx.doi.org/10.1080/00268976.2013.837534 P.-A. Artola and B. Rousseau "Thermal diffusion in simple liquid mixtures: what have we learnt from molecular dynamics simulations?", Molecular Physics '''111''' pp. 3394-3403 (2013)]
*[http://dx.doi.org/10.1063/1.4921958  Sunghan Roh, Juyeon Yi and Yong Woon Kim "Analysis of diffusion trajectories of anisotropic objects", Journal of Chemical Physics '''142''' 214302 (2015)]
 
[[Category: Non-equilibrium thermodynamics]]
[[Category: Non-equilibrium thermodynamics]]

Latest revision as of 12:05, 23 June 2015

Diffusion is the process behind Brownian motion. It was described by Albert Einstein in one of his annus mirabilis papers of 1905. What follows applies to homogeneous systems, see diffusion at interfaces for a non-homogeneous case.

The diffusion equation that describes this process is

where is the (self-)diffusion coefficient. For initial conditions for a Dirac delta function at the origin, and boundary conditions that force the vanishing of and its gradient at large distances, the solution factorizes as , with a spreading Gaussian for each of the Cartesian components:

Einstein relation[edit]

It follows from the previous equation that, for each of the Cartesian components, e.g. :

,

for every particle . Therefore, an average over all particles can be employed in order to improve statistics. The same applies to time averaging: in equilibrium the average from to must equal the average from to , so several time segments from the same simulation may be averaged for a given interval [2]. Adding all components, the following also applies:

Green-Kubo relation[edit]

Main article: Green-Kubo relations

where is the center of mass velocity of molecule . Note that this connect the diffusion coefficient with the velocity autocorrelation.

See also[edit]

References[edit]

Related reading