We construct a full-dimensional ab initio neural
network potential energy surface (PES) for the isomerization system
of the formic acid dimer (FAD). This is based upon ab initio calculations using the DLPNO-CCSD(T) approach with the aug-cc-pVTZ
basis set, performed at over 14000 symmetry-unique geometries. An
accurate fit to the obtained energies is generated using a general
neural network fitting procedure combined with the fundamental invariant
method, and the overall energy-weighted root-mean-square fitting error
is about 6.4 cm–1. Using this PES, we present a
multidimensional quantum dynamics study on tunneling splittings with
an efficient theoretical scheme developed by our group. The ground-state
tunneling splitting of FAD calculated with a four-mode coupled method
is in good agreement with the most recent experimental measurements.
The PES can be applied for further dynamics studies. The effectiveness
of the present scheme for constructing a high-dimensional PES is demonstrated,
and this scheme is expected to be feasible for larger molecular systems.