The fluctuation-dissipation theorem (FDT) is a powerful tool in statistical physics for predicting the behavior of non-equilibrium thermodynamical systems. These systems involve the irreversible dissipation of energy into heat from their reversible thermal fluctuations at thermodynamic equilibrium. 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. Often the linear response takes the form of one or more exponential decays.
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.
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.
linking D, the diffusion constant, and μ, the mobility of the particles. (μ is the ratio of the particle's terminal drift velocity to an applied force, μ = vd / F). kB ≈ 1.38065 × 10−23 m2 kg s−2 K−1 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, , and the bandwidth over which the voltage is measured:
The fluctuation-dissipation theorem can be formulated in many ways; one particularly useful form is the following:
Let be an observable of a dynamical system with Hamiltonian subject to thermal fluctuations. The observable will fluctuate around its mean value with fluctuations characterized by a power spectrum . Suppose that we can switch on a scalar field which alters the Hamiltonian to . The response of the observable to a time-dependent field is characterized to first order by the susceptibility or linear response function of the system
where the perturbation is adiabatically switched on at .
The fluctuation-dissipation theorem relates the two-sided power spectrum of to the imaginary part of the Fourier transform of the susceptibility :
The left-hand side describes fluctuations in , the right-hand side is closely related to the energy dissipated by the system when pumped by an oscillatory field .
This is the classical form of the theorem; quantum fluctuations are taken into account by replacing with (whose limit for is ). 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.Template:H. B. Callen and T. A. Welton, Phys. Rev. 83, (1951) 34
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
The probability distribution function W(x,0) is an equilibrium distribution and hence given by the Boltzmann distribution for the Hamiltonian
where . For a weak field , we can expand the right-hand side
here is the equilibrium distribution in the absence of a field. Plugging this approximation in the formula for 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 using the susceptibility of the system and hence find with the above equation (*)
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 is real and symmetric, it follows that
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.
- Non-equilibrium thermodynamics
- Green-Kubo relations
- Onsager reciprocal relations
- Equipartition theorem
- Boltzmann factor
- Dissipative system
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- Kubo's famous text: Fluctuation-dissipation theorem
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