Neural Network Potential Energy Surface for the low temperature Ring Polymer Molecular Dynamics of the H2CO + OH reaction

Autor: Pablo del Mazo-Sevillano, Octavio Roncero, Alfredo Aguado
Přispěvatelé: Ministerio de Ciencia, Innovación y Universidades (España), Centro de Supercomputación de Galicia, Red Española de Supercomputación
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Digital.CSIC. Repositorio Institucional del CSIC
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Popis: 14 pags., 12 figs., 2 tabs.
A new potential energy surface (PES) and dynamical study of the reactive process of H2CO + OH toward the formation of HCO + H2O and HCOOH + H are presented. In this work, a source of spurious long range interactions in symmetry adapted neural network (NN) schemes is identified, which 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 network scheme that prevents spurious asymptotic interactions. Quasi-classical trajectory (QCT) 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 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 with excitations of the normal modes of the ring polymer.
This research was funded by the MICIU (Spain) under Grant No. FIS2017-83473-C2. We also acknowledge computing time at Finisterre (CESGA) and MareNostrum (BSC) under the RES computational grant (Nos. ACCT-2019-3-0004 and AECT-2020-1- 0003).
Databáze: OpenAIRE