  We discuss a theoretical approach that employs machine learning potential
energy surfaces (ML-PESs) in the nonadiabatic dynamics simulation of polyatomic
systems by taking 6-aminopyrimidine as a typical example. The Zhu-Nakamura
theory is employed in the surface hopping dynamics, which does not require the
calculation of the nonadiabatic coupling vectors. The kernel ridge regression
is used in the construction of the adiabatic PESs. In the nonadiabatic dynamics
simulation, we use ML-PESs for most geometries and switch back to the
electronic structure calculations for a few geometries either near the S1/S0
conical intersections or in the out-of-confidence regions. The dynamics results
based on ML-PESs are consistent with those based on CASSCF PESs. The ML-PESs
are further used to achieve the highly efficient massive dynamics simulations
with a large number of trajectories. This work displays the powerful role of ML
methods in the nonadiabatic dynamics simulation of polyatomic systems.

Chemical Physics