Atomic-Level Features for Kinetic Monte Carlo Models of Complex Chemistry from Molecular Dynamics Simulations

Abstract

The high computational cost of evaluating atomic interactions recently motivated the development of computationally inexpensive kinetic models, which can be parameterized from molecular dynamics (MD) simulations of the complex chemistry of thousands of species or other processes and accelerate the prediction of the chemical evolution by up to four orders of magnitude. Such models go beyond the commonly employed potential energy surface fitting methods in that they are aimed purely at describing kinetic effects. So far, such kinetic models utilize molecular descriptions of reactions and have been constrained to only reproduce molecules previously observed in MD simulations. Therefore, these descriptions fail to predict the reactivity of unobserved molecules, for example, in the case of large molecules or solids. Here, we propose a new approach for the extraction of reaction mechanisms and reaction rates from MD simulations, namely, the use of atomic-level features. Using the complex chemical network of hydrocarbon pyrolysis as an example, it is demonstrated that kinetic models built using atomic features are able to explore chemical reaction pathways never observed in the MD simulations used to parameterize them, a critical feature to describe rare events. Atomic-level features are shown to construct reaction mechanisms and estimate reaction rates of unknown molecular species from elementary atomic events. Through comparisons of the model ability to extrapolate to longer simulation time scales and different chemical compositions than the ones used for parameterization, it is demonstrated that kinetic models employing atomic features retain the same level of accuracy and transferability as the use of features based on molecular species, while being more compact and parameterized with less data. We also find that atomic features can better describe the formation of large molecules enabling the simultaneous description of small molecules and condensed phases.

Temperature Extrapolation of Molecular Dynamics Simulations of Complex Chemistry to Microsecond Timescales using Kinetic Models: Applications to Hydrocarbon Pyrolysis

“…This assumption was made because the reactions with 4 or more atoms represented less 0.2% of the total reactions and increased significantly the KMC simulation run time. Accounting for reactions with 3 atoms is the main difference from our previous work [10] where reactions were only considered to be the breaking or the creation of a single bond. This modification was made to be closer to the definition of an elementary reaction which is the rearrangement of bonds occurring within one collision and which authorizes more than 2 atoms to be involved in a reaction.…”

“…Once the bonds were computed at each timestep, reactions could be extracted by finding bond rearrangements in the MD simulation. The reactions were represented using a formalism close to the "atomic features" described in previous work [10] with some adjustments that are recounted here. The "atomic features" framework describes reactions based on the atoms surrounding the reactive site.…”

“…As this method relies on Transition State Theory for gas phase, it cannot be applied to dense phase, where a widely-accepted Transition State Theory framework has not been developed yet. Another approach [10][11][12][13][14][15][16][17][18] to circumvent the short timescale of atomistic simulations is to simply increase the temperature of the system, because at higher temperatures, chemical reactions occur more often leading to shorter equilibration times. This approach has been used to study hydrocarbon pyrolysis [10][11][12][13][14][15], hydrogen combustion [16], methane oxidation [17], and hydrogen peroxide decomposition [18].…”

“…Another approach [10][11][12][13][14][15][16][17][18] to circumvent the short timescale of atomistic simulations is to simply increase the temperature of the system, because at higher temperatures, chemical reactions occur more often leading to shorter equilibration times. This approach has been used to study hydrocarbon pyrolysis [10][11][12][13][14][15], hydrogen combustion [16], methane oxidation [17], and hydrogen peroxide decomposition [18]. However, this method can only be applied above a certain temperature, where the equilibration time is considered reasonably small to be run in an MD simulation.…”

“…II, the full methodology to construct the kinetic model and extrapolate in temperature is detailed. In particular, we use an atomic-level description of reactions developed in our previous work [10] that was shown to condense the information in fewer reactions, predict more accurately the evolution of longer carbon chains and be able to predict the creation of molecules not observed in the atomistic simulation used for training. The temperature extrapolation was performed with an Arrhenius or a zero-barrier model.…”