The fluctuationdissipation 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 fluctuationdissipation theorem applies both to classical and quantum mechanical systems.
The fluctuationdissipation 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 nonequilibrium state to its statistical fluctuation properties in equilibrium. Often the linear response takes the form of one or more exponential decays.
The fluctuationdissipation theorem was originally formulated by Harry Nyquist in 1928, and later proven by Herbert Callen and Theodore A. Welton in 1951.
The fluctuationdissipation 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:
Drag and Brownian motion
Resistance and Johnson noise
Light absorption and thermal radiation
The fluctuationdissipation 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.
From this observation Einstein was able to use statistical mechanics to derive the EinsteinSmoluchowski relation
D
=
μ
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. k_{B} is the Boltzmann constant, and T is the absolute temperature.
In 1928, John B. Johnson discovered and Harry Nyquist explained Johnson–Nyquist noise. With no applied current, the meansquare voltage depends on the resistance R,
k
B
T
, and the bandwidth
Δ
ν
over which the voltage is measured:
⟨
V
2
⟩
=
4
R
k
B
T
Δ
ν
.
The fluctuationdissipation 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 timevarying, 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 timedependent field
f
(
t
)
is characterized to first order by the susceptibility or linear response function
χ
(
t
)
of the system
⟨
x
(
t
)
⟩
=
⟨
x
⟩
0
+
∫
−
∞
t
f
(
τ
)
χ
(
t
−
τ
)
d
τ
,
where the perturbation is adiabatically (very slowly) switched on at
τ
=
−
∞
.
The fluctuationdissipation theorem relates the twosided power spectrum of
x
to the imaginary part of the Fourier transform
χ
^
(
ω
)
of the susceptibility
χ
(
t
)
:
S
x
(
ω
)
=
2
k
B
T
ω
I
m
χ
^
(
ω
)
.
The lefthand side describes fluctuations in
x
, the righthand 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 fluctuationdissipation theorem can be generalized in a straightforward way to the case of spacedependent fields, to the case of several variables or to a quantummechanics setting.
We derive the fluctuationdissipation 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
θ
(
−
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
)
⟨
x
(
t
)
⟩
=
∫
d
x
′
∫
d
x
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
)
=
exp
(
−
β
H
(
x
)
)
∫
d
x
′
exp
(
−
β
H
(
x
′
)
)
,
where
β
−
1
=
k
B
T
. For a weak field
β
x
f
0
≪
1
, we can expand the righthand side
W
(
x
,
0
)
≈
W
0
(
x
)
(
1
+
β
f
0
x
)
,
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 autocorrelation function of x in the absence of a field:
A
(
t
)
=
⟨
x
(
t
)
x
(
0
)
⟩
0
.
Note that in the absence of a field the system is invariant under timeshifts. We can rewrite
⟨
x
(
t
)
⟩
−
⟨
x
⟩
0
using the susceptibility of the system and hence find with the above equation (*)
f
0
∫
0
∞
d
τ
χ
(
τ
)
θ
(
τ
−
t
)
=
β
f
0
A
(
t
)
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
−
χ
^
(
ω
)
=
i
ω
β
∫
0
∞
e
−
i
ω
t
A
(
t
)
d
t
−
β
A
(
0
)
.
Since
A
(
t
)
is real and symmetric, it follows that
2
I
m
[
χ
^
(
ω
)
]
=
ω
β
A
^
(
ω
)
.
Finally, for stationary processes, the WienerKhinchin theorem states that the twosided spectral density is equal to the Fourier transform of the autocorrelation function:
S
x
(
ω
)
=
A
^
(
ω
)
.
Therefore, it follows that
S
x
(
ω
)
=
2
k
B
T
ω
I
m
[
χ
^
(
ω
)
]
.
While the fluctuationdissipation 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 timescales to be studied while they remain in disequilibrium.
In the mid 1990s, in the study of nonequilibrium dynamics of spin glass models, a generalization of the fluctuationdissipation theorem was discovered that holds for asymptotic nonstationary states, where the temperature appearing in the equilibrium relation is substituted by an effective temperature with a nontrivial dependence on the time scales. This relation is proposed to hold in glassy systems beyond the models for which it was initially found.