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We discuss a theoretical approach that employs machine learning potentialenergy surfaces (ML-PESs) in the nonadiabatic dynamics simulation of polyatomicsystems by taking 6-aminopyrimidine as a typical example. The Zhu-Nakamuratheory is employed in the surface hopping dynamics, which does not require thecalculation of the nonadiabatic coupling vectors. The kernel ridge regressionis used in the construction of the adiabatic PESs. In the nonadiabatic dynamicssimulation, we use ML-PESs for most geometries and switch back to theelectronic structure calculations for a few geometries either near the S1/S0conical intersections or in the out-of-confidence regions. The dynamics resultsbased on ML-PESs are consistent with those based on CASSCF PESs. The ML-PESsare further used to achieve the highly efficient massive dynamics simulationswith a large number of trajectories. This work displays the powerful role of MLmethods in the nonadiabatic dynamics simulation of polyatomic systems.

Inclusion of machine learning kernel ridge regression potential energy surfaces in on-the-fly nonadiabatic molecular dynamics simulation