Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Artificial Neural Networks

Nonadiabatic (NA) molecular dynamics
(MD) allows one to study far-from-equilibrium
processes involving excited electronic states coupled to atomic motions.
While NAMD involves expensive calculations of excitation energies
and NA couplings (NACs), ground-state properties require much less
effort and can be obtained with machine learning (ML) at a fraction
of the ab initio cost. Application of ML to excited states and NACs
is more challenging, due to costly reference methods, many states,
and complex geometry dependence. We developed a NAMD methodology that
avoids time extrapolation of excitation energies and NACs. Instead,
under the classical path approximation that employs a precomputed
ground-state trajectory, we use a small fraction (2%) of the geometries
to train neural networks and obtain excited-state energies and NACs
for the remaining 98% of the geometries by interpolation. Demonstrated
with metal halide perovskites that exhibit complex MD, the method
provides nearly two orders of computational savings while generating
accurate NAMD results.