GEM4 Summer School OpenCourseWare Given …...Fluctuation-dissipation theorem (Green-Kubo formula)...
Transcript of GEM4 Summer School OpenCourseWare Given …...Fluctuation-dissipation theorem (Green-Kubo formula)...
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GEM4 Summer School OpenCourseWare http://gem4.educommons.net/ http://www.gem4.org/
Lecture: “Thermal Forces and Brownian Motion” by Ju Li. Given August 11, 2006 during the GEM4 session at MIT in Cambridge, MA.
Please use the following citation format:
Li, Ju. “Thermal Forces and Brownian Motion.” Lecture, GEM4 session at MIT, Cambridge, MA, August 11, 2006. http://gem4.educommons.net/ (accessed MM DD, YYYY). License: Creative Commons Attribution-Noncommercial-Share Alike.
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Thermal Forces and Brownian Motion
Ju Li
GEM4 Summer School 2006 Cell and Molecular Mechanics in BioMedicine August 7–18, 2006, MIT, Cambridge, MA, USA
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Outline
• Meaning of the Central Limit Theorem
• Diffusion vs Langevin equation descriptions
(average vs individual)
• Diffusion coefficient and fluctuation-dissipation theorem
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Central Limit Theorem Y = X1 + X2 + … + XN
X1, X2, …, XN are random variables
E[Y] = E[X1] + E[X2] + … + E[XN]
If X1, X2, …, XN are independent random variables:
var[Y] = var[X1] + var[X2] + … + var[XN]
Note: var[X] = σ2 X ≡ E[ (X-E[X])2 ]
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If X1, X2, …, XN are independent random variables sampled from the same
distribution: E[Y] = NE[X]
var[Y] = N var[X1] = Nσ2 X
Average of the sum: y ≡ Y/N E[y] = E[X], var[y] = var[Y]/N2 = σ2
X / N
Law of large numbers: as N gets large, the average of the sum becomes more and
more deterministic, with variance σ2 X / N. 4
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X1, X2, …, XN may be sampled from Probability density
-1 2 X
X
Probability density
Probability density
X 5
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We know the probability distribution of Y is shifting (NE[X]), as well as getting fat
(Nσ2 X). But how about its shape ?
The central limit theorem says that irrespective of the shape of X,
6 Y
Probability density
NE[X])
Nσ2X
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Why Gaussian ?
large N (Y N [X ]) 2 ρ( ) →
1 − EY π σ2 N X
2 exp
2Nσ X
2
Gaussian is special (Maxwellian velocity distribution, etc).
While proof is involved, here we note that Gaussian is an invariant
shape (attractor in shape space) in the mathematical operation of convolution.
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Diffusion Equation in 1D J
∂ tρ x ( D x ) ∂2 xρ= − ∂ − ∂ ρ = D
Random walker view of diffusion: imagine (a) We release the walker at x=0 at t=0,
(b) Walker makes a move of ±a, with equal probability, every ∆t=1/ν from then on.
Mathematically, we say ρ(x,t=0)=δ(x). t
N = =ν t independent random steps ∆t
Then, ( ) x1 x2 ... xt / ∆tx t = ∆ + ∆ + + ∆ 8
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When N=ν t >>1, the central limit theorem applies:
E[x(t)] = 0, var[x(t)] = ν t var[∆ x] = ν ta2
So we can directly write down ρ( x t( )) as 1 x2
ρG ( x t, ) = exp 2 22πν a t 2ν a t
It is the probability of finding the walker at x at time t, knowing he was at 0 at time 0.
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By plugging in, we can directly verify ρG x t, satisfies( )
ρ D 2ρ ( δ (x). ∂ = ∂ , ρ x,0) =t x 2
with macroscopic D identified as va .2
1 x2 ρ x t, =
π exp G ( )2 (2 Dt) 2(2 Dt)
is called Green's function solution to diffusion equation.
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Brownian Motion
Courtesy of Microscopy-UK. Used with permission.
Fat droplets suspended in milk (from Dave Walker). The droplets range in size from about 0.5 to 3 µm. 11
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viscous oil
v Stokes' law: F=-6πrηv=-λv
mv = F = −λv, v t ( = 0) = v0 λ
− t m→ v t( ) = v e 0
Einstein's Explanation of Brownian Motion 2mv k T Also, equi-partition theorem: = B
2 2
In addition to dissipative force, there must be another, stimulative force. 12
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mv = Fdissipative + Fstimulative/fluctuation = −λv + Ffluc ( )t
Ffluc ( )t = 0
Ffluc ( )t F fluc ( ) t′ = b t t ′)( −
If b t t ) = Bδ ( − t t ′) : white noise ( − ′
Exact Green's function solution of v t( ): t −
λ (t t− ′)v t( ) =
1 ∫−∞
′ t e ′ mdt F fluc ( ) m
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( ) ( ) v t v t
t −λ
− ′) −λ
− ′)1 m m= dt F ′ fluc ( )t e ′ (t t t
dt fluc t e (t t
2 ∫−∞ ∫−∞ ′F ( )′
m
= 2 ∫t
dt′e (t t )
∫t
dt′e (t t )1 −
λ− ′ −
λ − ′
m m t F Ffluc ( )′ fluc ( )t′ m −∞ −∞
1 t −λ (t t t −
λ (t t − ′= dt′e
− ′) dt′e
) δ ( ′ − t2 ∫−∞
m ∫−∞ m B t ′)
m t (t t (t t
m m= 1
2 ∫−∞ dt
−λ
− ′) H t t e ′)
−λ − ′)
B′e ( − m ( ) is Heaviside step function: H x
1 if x > 0−t tB −λ
( ) = = e m H x 0 if x ≤ 0
2mλ 14
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BIn particular: ( ) ( ) v t v t = 2mλ
However, from equilibrium statistical mechanics: equi-partition theorem:
m v t v t ( ) ( ) = k T B
B → = k T
2λ B
The ratio between square of stimulative force and dissipative force is fixed, ∝ T
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t t−k T − λ
( ) ( ) v t v t = B e m
m
Previously, from the Gaussian solution to ρ D 2ρ , ρ(x,0) = δ x) : ∂ = ∂ (t x
1 x2 x t = exp ρG ( ), 2 (2 π Dt) 2(2 Dt)
we know if the particle is released at x = 0 at t = 0 : ( ) ( ) x t x t = 2Dt
t
∫0 ′ ′
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x t( ) = +0 dt v t ( ), x t( ) = v t( )
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d ( ) ( ) = 2 ( ) ( ) = 2 ( ) ( )x t x t x t x t x t v t dt
d = (2Dt) = 2D
dt
tD = ( ) ( )x t v t = ′ ′( ) v t ( )dt v t (∫ )0
t ′( ) ( ) = dt v t v t ∫0
′t
′( ) (0) = dt v t v ∫0 ′
Velocity auto-correlation function: g t( ) ≡ v t v ( ) (0) 17
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Actually, the onset of macroscopic diffusion (∂ = ∂ρ D 2ρ ) is only valid only when t x
t intrinsic timescale of g t ( ) ∝ m
λ (Same as central limit theorem in random walk)
So the correct formula is ∞
′D = ∫0 dt ′ v t ( ) (0) v
The above is one of the fluctuation-dissipation theorems.
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Thermal conductivity: κ = 1
2 ∫0
∞ Jq ( ) t Jq (0) dt
Ωk TB
1 ∞Electrical conductivity: σ =
Ωk T ∫0 J ( ) (0)t J dt
B
Shear viscosity: η =Ω ∞
τ xy ( ) t τ xy (0) dt k T ∫0
B
Fluctuation-dissipation theorem (Green-Kubo formula) is one of the most elegant and
significant results of statistical mechanics. It relates transport properties (system behavior if
linearly perturbed from equilibrium) to the time-correlation of equilibrium fluctuations. 19
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Coming back to diffusion (mass transport): −t tk T − λ
( ) ( ) v t v t = B e m
m ∞ k T′d ′So D = ∫0
t v t v ( ) (0) = B .λ
1 is actually the mobility of the particle, whenλ
driven by external (non-thermal) force.
D = k T is calle d the Einstein relation,
1/λ B
first derived in 1905. 20
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References
Kubo, Toda & Hashitume, Statistical Physics II: Nonequilibrium Statistical Mechanics
(Springer-Verlag, New York, 1992).
Zwanzig, Nonequilibrium Statistical Mechanics (Oxford University Press, Oxford, 2001).
van Kampen, Stochastic processes in physics and chemistry, rev. and enl. ed. (North-Holland, Amsterdam, 1992).
Reichl, A modern course in statistical physics (Wiley, New York, 1998).
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