Nonadiabatic molecular dynamics (NAMD) simulations of
molecular
systems require the efficient evaluation of excited-state properties,
such as energies, gradients, and nonadiabatic coupling vectors. Here,
we investigate the use of graphics processing units (GPUs) in addition
to central processing units (CPUs) to efficiently calculate these
properties at the time-dependent density functional theory (TDDFT)
level of theory. Our implementation in the FermiONs++ program package
uses the J-engine and a preselective screening procedure for the calculation
of Coulomb and exchange kernels, respectively. We observe good speed-ups
for small and large molecular systems (comparable to those observed
in ground-state calculations) and reduced (down to sublinear) scaling
behavior with respect to the system size (depending on the spatial
locality of the investigated excitation). As a first illustrative
application, we present efficient NAMD simulations of a series of
newly designed light-driven rotary molecular motors and compare their
S1 lifetimes. Although all four rotors show different S1 excitation energies, their ability to rotate upon excitation
is conserved, making the series an interesting starting point for
rotary molecular motors with tunable excitation energies.