First-principles-based reaction kinetics from reactive molecular dynamics simulations: Application to hydrogen peroxide decomposition

Abstract

This paper presents our vision of how to use in silico approaches to extract the reaction mechanisms and kinetic parameters for complex condensed-phase chemical processes that underlie important technologies ranging from combustion to chemical vapor deposition. The goal is to provide an analytic description of the detailed evolution of a complex chemical system from reactants through various intermediates to products, so that one could optimize the efficiency of the reactive processes to produce the desired products and avoid unwanted side products. We could start with quantum mechanics (QM) to ensure an accurate description; however, to obtain useful kinetics we need to average over ∼10-nm spatial scales for ∼1 ns, which is prohibitively impractical with QM. Instead, we use the reactive force field (ReaxFF) trained to fit QM to carry out the reactive molecular dynamics (RMD). We focus here on showing that it is practical to extract from such RMD the reaction mechanisms and kinetics information needed to describe the reactions analytically. This analytic description can then be used to incorporate the correct reaction chemistry from the QM/ReaxFF atomistic description into larger-scale simulations of ∼10 nm to micrometers to millimeters to meters using analytic approaches of computational fluid dynamics and/or continuum chemical dynamics. In the paper we lay out the strategy to extract the mechanisms and rate parameters automatically without the necessity of knowing any details of the chemistry. We consider this to be a proof of concept. We refer to the process as RMD2Kin (reactive molecular dynamics to kinetics) for the general approach and as ReaxMD2Kin (ReaxFF molecular dynamics to kinetics) for QM-ReaxFF-based reaction kinetics.

Keywords: RMD2Kin; ReaxFF reactive force field; ReaxMD2Kin; reaction kinetics; reactive molecular dynamics.

Fig. 1.

Observed (solid line) and predicted (dashed line) species as a function of time, for several temperatures from 1,000 K to 2,000 K (other temperatures are in SI Appendix, Fig. S1). Observed data are from the ReaxFF RMD; predicted data are the analytical extrapolation from initial concentrations using ($\mathrm{\Delta }{\mathit{H}}^{‡}$) and ($\mathrm{\Delta }{\mathit{S}}^{‡}$) determined from fitting rate constants to temperature.

Fig. 2.

Calculated rate constants vs. average system temperature, extracted from the observed ReaxFF RMD and plotted as ln($k$/T) vs 1/T. The best least-squares linear fit to these data is shown (dashed line), which was used to determine the ($\mathrm{\Delta }{\mathit{H}}^{‡}$) and ($\mathrm{\Delta }{\mathit{S}}^{‡}$) for Eq. 1.

Fig. 3.

Reaction steps extracted from the ReaxFF reaction trajectories. (A) Reaction 2 of HOO and HOOH to produce OH, ${\mathrm{O}}_2$, and ${\mathrm{H}}_2$O. (B) Another collision geometry of HOO and HOOH resulting in a potential exchange of H between HOO and HOOH but no net reaction. (C) Reaction 7 showing the rotationally bound HO–OH complex that dissociates after hundreds of femtoseconds.