Fluctuation dissipation theorem

Qualitative overview and examples

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:

Examples in detail

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

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

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

Thermal noise in a resistor

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 B T , and the bandwidth Δ ν over which the voltage is measured:

General formulation

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

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 ⟨ x ⟩ 0 with fluctuations characterized by a power spectrum S x ( ω ) = ⟨ x ^ ( ω ) x ^ ∗ ( ω ) ⟩ . 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 χ ( t ) of the system

where the perturbation is adiabatically (very slowly) switched on at τ = − ∞ .

The fluctuation-dissipation theorem relates the two-sided power spectrum of x to the imaginary part of the Fourier transform χ ^ ( ω ) of the susceptibility χ ( t ) :

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 ⁡ ( ω t + ϕ ) .

This is the classical form of the theorem; quantum fluctuations are taken into account by replacing 2 k B T / ω with ℏ coth ⁡ ( ℏ ω / 2 k B T ) (whose limit for ℏ → 0 is 2 k B T / ω ). A proof can be found by means of the LSZ reduction, an identity from quantum field theory.

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.

Derivation

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

We can express the expectation value of x by the probability distribution W(x,0) and the transition probability P ( x ′ , t | 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

where β − 1 = k B T . For a weak field β x f 0 ≪ 1 , we can expand the right-hand side

here W 0 ( x ) is the equilibrium distribution in the absence of a field. Plugging this approximation in the formula for ⟨ x ( t ) ⟩ yields

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

Note that in the absence of a field the system is invariant under time-shifts. We can rewrite ⟨ x ( t ) ⟩ − ⟨ x ⟩ 0 using the susceptibility of the system and hence find with the above equation (*)

Consequently,

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

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

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:

Therefore, it follows that

Violations in glassy systems

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 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.