Fluctuation-dissipation theorem

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The fluctuation-dissipation theorem (FDT) is a powerful tool in statistical physics for predicting the behavior of systems that obey detailed balance. Given that a system obeys detailed balance, the theorem is a general proof that thermal fluctuations in a physical variable predict the response quantified by the admittance or impedance of the same physical variable, and vice versa. The fluctuation-dissipation theorem applies both to classical and quantum mechanical systems.

The fluctuation-dissipation theorem relies on the assumption that the response of a system in thermodynamic equilibrium to a small applied force is the same as its response to a spontaneous fluctuation. Therefore, the theorem connects the linear response relaxation of a system from a prepared non-equilibrium state to its statistical fluctuation properties in equilibrium.[1] Often the linear response takes the form of one or more exponential decays.

The fluctuation-dissipation theorem was originally formulated by Harry Nyquist in 1928,[2] and later proven by Herbert Callen and Theodore A. Welton in 1951.[3]

Qualitative overview and examples[edit]

The fluctuation-dissipation theorem says that when there is a process that dissipates energy, turning it into heat (e.g., friction), there is a reverse process related to thermal fluctuations. This is best understood by considering some examples:

If an object is moving through a fluid, it experiences drag (air resistance or fluid resistance). Drag dissipates kinetic energy, turning it into heat. The corresponding fluctuation is Brownian motion. An object in a fluid does not sit still, but rather moves around with a small and rapidly-changing velocity, as molecules in the fluid bump into it. Brownian motion converts heat energy into kinetic energy—the reverse of drag.
If electric current is running through a wire loop with a resistor in it, the current will rapidly go to zero because of the resistance. Resistance dissipates electrical energy, turning it into heat (Joule heating). The corresponding fluctuation is Johnson noise. A wire loop with a resistor in it does not actually have zero current, it has a small and rapidly-fluctuating current caused by the thermal fluctuations of the electrons and atoms in the resistor. Johnson noise converts heat energy into electrical energy—the reverse of resistance.
When light hits an object, some fraction of the light is absorbed, making the object hotter. In this way, light absorption turns light energy into heat. The corresponding fluctuation is thermal radiation (e.g., the glow of a "red hot" object). Thermal radiation turns heat energy into light energy—the reverse of light absorption. Indeed, Kirchhoff's law of thermal radiation confirms that the more effectively an object absorbs light, the more thermal radiation it emits.

Examples in detail[edit]

The fluctuation-dissipation theorem is a general result of statistical thermodynamics that quantifies the relation between the fluctuations in a system at thermal equilibrium and the response of the system to applied perturbations.

The model thus allows, for example, the use of molecular models to predict material properties in the context of linear response theory. The theorem assumes that applied perturbations, e.g., mechanical forces or electric fields, are weak enough that rates of relaxation remain unchanged.

Brownian motion[edit]

For example, Albert Einstein noted in his 1905 paper on Brownian motion that the same random forces that cause the erratic motion of a particle in Brownian motion would also cause drag if the particle were pulled through the fluid. In other words, the fluctuation of the particle at rest has the same origin as the dissipative frictional force one must do work against, if one tries to perturb the system in a particular direction.

From this observation Einstein was able to use statistical mechanics to derive the Einstein-Smoluchowski relation

 D =  {\mu \, k_B T}

which connects the diffusion constant D and the particle mobility μ, the ratio of the particle's terminal drift velocity to an applied force. kB is the Boltzmann constant, and T is the absolute temperature.

Thermal noise in a resistor[edit]

In 1928, John B. Johnson discovered and Harry Nyquist explained Johnson–Nyquist noise. With no applied current, the mean-square voltage depends on the resistance R, k_BT, and the bandwidth \Delta\nu over which the voltage is measured:

 \langle V^2 \rangle = 4Rk_BT\,\Delta\nu.

General formulation[edit]

The fluctuation-dissipation theorem can be formulated in many ways; one particularly useful form is the following:[citation needed]

Let x(t) be an observable of a dynamical system with Hamiltonian H_0(x) subject to thermal fluctuations. The observable x(t) will fluctuate around its mean value \langle x\rangle_0 with fluctuations characterized by a power spectrum S_x(\omega) = \langle \hat{x}(\omega)\hat{x}^*(\omega) \rangle. Suppose that we can switch on a time-varying, spatially constant field f(t) which alters the Hamiltonian to H(x)=H_0(x)+f(t)x. The response of the observable x(t) to a time-dependent field f(t) is characterized to first order by the susceptibility or linear response function \chi(t) of the system

 \langle x(t) \rangle = \langle x \rangle_0 + \int\limits_{-\infty}^{t} \! f(\tau) \chi(t-\tau)\,d\tau,

where the perturbation is adiabatically[clarification needed] switched on at \tau =-\infty.

The fluctuation-dissipation theorem relates the two-sided[clarification needed] power spectrum of x to the imaginary part of the Fourier transform \hat{\chi}(\omega) of the susceptibility \chi(t):

S_x(\omega) = \frac{2 k_\mathrm{B} T}{\omega} \mathrm{Im}\,\hat{\chi}(\omega).

The left-hand side describes fluctuations in x, the right-hand side is closely related to the energy dissipated by the system when pumped by an oscillatory field f(t) = F \sin(\omega t + \phi).

This is the classical form of the theorem; quantum fluctuations are taken into account by replacing 2 k_\mathrm{B} T/\omega with {\hbar}\,\coth(\hbar\omega/2k_\mathrm{B}T) (whose limit for \hbar\to 0 is 2 k_\mathrm{B} T/\omega). A proof can be found by means of the LSZ reduction, an identity from quantum field theory.[citation needed]

The fluctuation-dissipation theorem can be generalized in a straightforward way to the case of space-dependent fields, to the case of several variables or to a quantum-mechanics setting.[3]


We derive the fluctuation-dissipation theorem in the form given above, using the same notation. Consider the following test case: The field f has been on for infinite time and is switched off at t=0

 f(t)=f_0 \theta(-t) .

We can express the expectation value of x by the probability distribution W(x,0) and the transition probability  P(x',t | x,0)

 \langle x(t) \rangle = \int dx' \int dx \, x' P(x',t|x,0) W(x,0) .

The probability distribution function W(x,0) is an equilibrium distribution and hence given by the Boltzmann distribution for the Hamiltonian  H(x)=H_0(x) - x f_0

 W(x,0)= \frac{\exp(-\beta H(x))}{\int dx' \, \exp(-\beta H(x'))} \;,

where \beta^{-1} = k_{\rm B}T. For a weak field  \beta x f_0 \ll 1 , we can expand the right-hand side

 W(x,0) \approx W_0(x) (1+\beta f_0 x),

here  W_0(x) is the equilibrium distribution in the absence of a field. Plugging this approximation in the formula for  \langle x(t) \rangle yields

\langle x(t) \rangle = \langle x \rangle_0 + \beta f_0 A(t),






where A(t) is the auto-correlation function of x in the absence of a field:

 A(t)=\langle x(t) x(0) \rangle_0.

Note that in the absence of a field the system is invariant under time-shifts. We can rewrite  \langle x(t) \rangle - \langle x \rangle_0 using the susceptibility of the system and hence find with the above equation (*)

 f_0 \int_0^{\infty} d\tau \, \chi(\tau) \theta(\tau-t) = \beta f_0 A(t)


-\chi(t) = \beta {\operatorname{d}A(t)\over\operatorname{d}t} 
\theta(t) .






To make a statement about frequency dependence, it is necessary to take the Fourier transform of equation (**). By integrating by parts, it is possible to show that

 -\hat\chi(\omega) = i\omega\beta \int\limits_0^\infty \mathrm{e}^{-i\omega t} A(t)\, dt -\beta A_0.

Since A(t) is real and symmetric, it follows that

 2\,\mathrm{Im}[\hat\chi(\omega)] = \omega\beta \hat A(\omega).

Finally, for stationary processes, the Wiener-Khinchin theorem states that the two-sided spectral density is equal to the Fourier transform of the auto-correlation function:

 S_x(\omega) = \hat{A}(\omega).

Therefore, it follows that

 S_x(\omega) = \frac{2k_\text{B} T}{\omega} \,\mathrm{Im}[\hat\chi(\omega)].

Violations in glassy systems[edit]

While the fluctuation-dissipation theorem provides a general relation between the response of equilibrium systems to small external perturbations and their spontaneous fluctuations, no general relation is known for systems out of equilibrium. Glassy systems at low temperatures, as well as real glasses, are characterized by slow approaches to equilibrium states. Thus these systems require large time-scales to be studied while they remain in disequilibrium.

In the mid 1990s, in the study of non-equilibrium dynamics of spin glass models, a generalization of the fluctuation-dissipation theorem was discovered[citation needed] that holds for asymptotic non-stationary states, where the temperature appearing in the equilibrium relation is substituted by an effective temperature with a non-trivial dependence on the time scales. This relation is proposed to hold in glassy systems beyond the models for which it was initially found.

See also[edit]


  1. ^ David Chandler (1987). Introduction to Modern Statistical Mechanics. Oxford University Press. p. 255. ISBN 978-0-19-504277-1. 
  2. ^ Nyquist H (1928). "Thermal Agitation of Electric Charge in Conductors". Physical Review 32: 110–113. Bibcode:1928PhRv...32..110N. doi:10.1103/PhysRev.32.110. 
  3. ^ a b H.B. Callen, T.A. Welton (1951). "Irreversibility and Generalized Noise". Physical Review 83: 34–40. Bibcode:1951PhRv...83...34C. doi:10.1103/PhysRev.83.34. 


Further reading[edit]

  • Chandler D (1987). Introduction to Modern Statistical Mechanics. Oxford University Press. pp. 231–265. ISBN 978-0-19-504277-1. 
  • Reichl LE (1980). A Modern Course in Statistical Physics. Austin TX: University of Texas Press. pp. 545–595. ISBN 0-292-75080-3. 
  • Plischke M, Bergersen B (1989). Equilibrium Statistical Physics. Englewood Cliffs, NJ: Prentice Hall. pp. 251–296. ISBN 0-13-283276-3. 
  • Huang K (1987). Statistical Mechanics. New York: John Wiley and Sons. pp. 153, 394–396. ISBN 0-471-81518-7. 
  • Callen HB (1985). Thermodynamics and an Introduction to Thermostatistics. New York: John Wiley and Sons. pp. 307–325. ISBN 0-471-86256-8.