Automatic mechanism generation tools such as RMG (Reaction Mechanism Generator) are very useful for accelerating the development of detailed chemical mechanisms. However, their accuracy relies on the chemical knowledge that has been incorporated by the developers. A large part of this chemical knowledge lies in the data for estimating thermochemistry or kinetics. The remainder is in how the algorithm interprets and applies those data when making predictions. In modeling the formation of polycyclic aromatic hydrocarbons (PAHs), it is vital for the mechanism generation algorithm to have some understanding of aromaticity and rings. This work describes recent improvements in RMG’s understanding of both concepts to improve the accuracy of reaction kinetics estimation. RMG relies primarily on rate rules for kinetics estimation, wherein a rate is assigned to a reaction based on the most similar template which matches the reaction. RMG previously used Kekulé structures to estimate rates for aromatic species, but can now use aromatic structures, enabling the definition of rate rules for aromatic bonds. Ring perception was also implemented for kinetics groups, allowing rate rules to be restricted to linear or cyclic species only. The combination of these two changes allows RMG to better differentiate between rates for PAHs from those for linear alkenes. Here, we present a few test cases to demonstrate the importance of this differentiation and the effect that these improvements have on model generation.

Improved aromaticity handling and ring perception in RMG to model PAH formation

256th National Meeting (2018)

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