  Quantum transport and other phenomena are typically modeled by coupling the
system of interest to an environment, or bath, held at thermal equilibrium.
Realistic bath models are at least as challenging to construct as models for
the quantum systems themselves, since they must incorporate many degrees of
freedom that interact with the system on a wide range of timescales. Owing to
computational limitations, the environment is often modeled with simple
functional forms, with a few parameters fit to experiment to yield
semi-quantitative results. Growing computational resources have enabled the
construction of more realistic bath models from molecular dynamics (MD)
simulations. In this paper, we develop a numerical technique to construct these
atomistic bath models with better accuracy and decreased cost. We apply a novel
signal processing technique, known as super-resolution, combined with a
dictionary of physically-motivated bath modes to derive spectral densities from
MD simulations. Our approach reduces the required simulation time and provides
a more accurate spectral density than can be obtained via standard Fourier
transform methods. Moreover, the spectral density is provided as a convenient
closed-form expression which yields an analytic time-dependent bath kernel.
Exciton dynamics of the Fenna-Matthews-Olsen light-harvesting complex are
simulated with a second order time-convolutionless master equation, and
spectral densities constructed via super-resolution are shown to reproduce the
dynamics using only a quarter of the amount of MD data.
