Nonadiabatic (NA) molecular dynamics
(MD) allows one to investigate
far-from-equilibrium processes in nanoscale and molecular materials
at the atomistic level and in the time domain, mimicking time-resolved
spectroscopic experiments. Ab initio NAMD is limited to about 100
atoms and a few picoseconds, due to computational cost of excitation
energies and NA couplings. We develop a straightforward methodology
that can extend ab initio quality NAMD to nanoseconds and thousands
of atoms. The ab initio NAMD Hamiltonian is sampled and interpolated
along a trajectory using a Fourier transform, and then, it is used
to perform NAMD with known algorithms. The methodology relies on the
classical path approximation, which holds for many materials and processes.
To achieve a complete ab initio quality description, the trajectory
can be obtained using an ab initio trained machine learning force
field. The method is demonstrated with charge carrier trapping and
relaxation in hybrid organic–inorganic and all-inorganic metal
halide perovskites that exhibit complex dynamics and are actively
studied for optoelectronic applications.