MSD of the Prony-GLE model.

prony_msd(t, lambda, nu = 1, ...)

Arguments

t

Vector of timepoints at which to calculate the MSD.

lambda

Vector of length K giving the inverse decorrelation times of the exponential decay terms.

nu

Autocorrelation scaling factor.

...

Additional arguments to prony_coeff().

Value

Vector of mean square displacements at the timepoints in t.

Details

The Prony-GLE model satisfies the integro-differential equation $$ F_t - \int_{-\infty}^t \kappa(t-s) \dot X_s ds = 0, \qquad ACF_F(t) = k_B T \kappa(t), $$ where \(T\) is temperature, \(k_B\) is Boltzmann's constant, and the memory kernel is a sum of exponentials: $$ \kappa(t) = \nu sum_{k=1}^K exp(-\lambda_k t). $$ The solution process is of the form $$ X_t = C_0 B_t + \sum_{i=1}^{K-1} C_i W_{it}, $$ where \(B_t\) is Brownian motion and \(d W_{it} = -\rho_i W_{it} dt + d B_{it}\) are Ornstein-Uhlenbeck processes all independent of each other and of \(B_t\).

Note

Since temperature is not provided, the result is only proportional to the desired MSD, such that

MSD_X(t) = k_B T * prony_msd(t, lambda, nu)