  The complexity of molecular dynamics simulations necessitates dimension
reduction and coarse-graining techniques to enable tractable computation. The
generalized Langevin equation (GLE) describes coarse-grained dynamics in
reduced dimensions. In spite of playing a crucial role in non-equilibrium
dynamics, the memory kernel of the GLE is often ignored because it is difficult
to characterize and expensive to solve. To address these issues, we construct a
data-driven rational approximation to the GLE. Building upon previous work
leveraging the GLE to simulate simple systems, we extend these results to more
complex molecules, whose many degrees of freedom and complicated dynamics
require approximation methods. We demonstrate the effectiveness of our
approximation by testing it against exact methods and comparing observables
such as autocorrelation and transition rates.
