Neural Network Potential Energy Surface for the low temperature Ring
  Polymer Molecular Dynamics of the H2CO + OH reaction
  A new potential energy surface (PES) and dynamical study are presented of the
reactive process between H2CO + OH towards the formation of HCO + H2O and HCOOH
+ H. In this work a source of spurious long range interactions in symmetry
adapted neural network (NN) schemes is identified, what prevents their direct
application for low temperature dynamical studies. For this reason, a partition
of the PES into a diabatic matrix plus a NN many body term has been used fitted
with a novel artificial neural networks scheme that prevents spurious
asymptotic interactions. Quasi-classical trajectory and ring polymer molecular
dynamics (RPMD) studies have been carried on this PES to evaluate the rate
constant temperature dependence for the different reactive processes, showing a
good agreement with the available experimental data. Of special interest is the
analysis of the previously identified trapping mechanism in the RPMD study,
which can be attributed to spurious resonances associated to excitations of the
normal modes of the ring polymer.
