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.