The ab initio nanoreactor is a novel approach for the automated discovery of chemical reactions and the corresponding species by means of accelerated molecular dynamics (MD) simulations. [1] Based solely on quantum chemical calculations it provides an intuitive and unbiased access to complex chemical reaction networks. We present a new implementation of the ab initio nanoreactor which is able to generate thermodynamic and kinetic data. In order to simulate the chemical kinetics of a given system, reaction rates base on transition state theory are calculated. The resulting reaction network is able to quantify and even predict the outcome of chemical experiments without ad hoc assumptions about the reaction mechanism. Further improvements like the inclusion of a geodesic interpolation to smoothen the reaction path are presented. Our implementation allows to easily change between different levels of theory for optimization, Hessian calculation and final single point calculation. The combustion of small organic molecules is used to demonstrate the capabilities of the nanoreactor to create large chemical reaction networks. The reaction databases include several tens of thousands of reactions connecting thousands of different chemical species. Kinetic modelling enables the comparison with established models and experimentally accessible quantities such as ignition delay times or product distributions. For combustion, we discovered many species which are not included in conventional networks. This helps to understand the combustion mechanisms under different conditions and makes it flexible towards extensions of the reaction database.

Construction of reaction networks using the ab initio nanoreaction coupled to a kinetic model

2019 Fall National Meeting

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