We use the equilibrium generalized Langevin equation and its generalized fluctuation-dissipation relation, with the factor in the noise autocorrelation fixing the equilibrium velocity variance. Let be the velocity relaxation function and let a tilde denote a one-sided Laplace transform:
The equilibrium velocity autocorrelation function is , so
For completeness, this relation follows without assuming independence of the noise and velocity in a stationary infinite history. The two-sided noise power spectral density is . The stable linear equation has velocity transfer function , so
This is precisely the power spectral density of the even extension of . Stable relaxation removes initial transients with a finite second moment, giving the long-time autocorrelation function defined in the question. All the parameter choices used below have stable relaxation.
Integrating the velocity autocorrelation function twice gives the mean squared displacement from a fixed initial position:
A decaying initial transient does not change its asymptotic slope. Therefore the Green-Kubo formula for diffusion gives
For the given friction memory kernel, direct integration yields
At , the integrated friction is , hence
The relaxation denominator here is . Its roots have negative real parts by the cubic Routh-Hurwitz criterion, since all coefficients are positive and . This justifies the decay and integrability used above. The diffusion coefficient depends on the area under the friction memory kernel, not just its value at zero.
For , , , the friction memory kernel and its Laplace transform are
Using the generalized fluctuation-dissipation relation with equilibrium velocity variance , the velocity autocorrelation function has Laplace transform
All three poles are negative, so the stationary relaxation is stable. A partial fraction decomposition gives
Taking the inverse Laplace transform gives
The negative middle coefficient is compatible with an autocorrelation function; individual terms in a partial fraction decomposition need not themselves be separate physical modes with positive weights. Several useful checks follow directly:
The Volterra integral equation for predicts exactly and . Its integral is also , agreeing with the Green-Kubo formula for diffusion.
Write , so the friction memory kernel is and has area one for every . Its Laplace transform is . The generalized fluctuation-dissipation relation therefore gives
To verify the pointwise time-domain limit, rather than relying only on Laplace transform convergence, consider . The two roots
are negative and satisfy , . Since and , the inverse Laplace transform is
The coefficient of the slow exponential tends to , and that of the fast exponential tends to zero. Hence
This is the velocity autocorrelation function of an Ornstein-Uhlenbeck process satisfying . The friction memory kernel becomes local in time. Consistently, the even noise autocorrelation tends to in the sense of distributions, fixing the white noise normalization. The finite- right derivative does not converge to the right derivative of the limiting function: there is a short initial relaxation layer.
The integrated friction is exactly
For every finite , the roots of have negative real parts. The Green-Kubo formula for diffusion therefore gives the diffusion coefficient directly:
Consequently
Integrating the limiting velocity autocorrelation function gives the same value, . Computing before taking the limit also avoids having to justify an interchange of the infinite-time integral and the parameter limit.

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