Predicting long timescale kinetics under variable experimental conditions with Kinetica.jl
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Predicting the degradation processes of molecules over long timescales is a key aspect of industrial materials design. However, it is made computationally challenging by the need to construct large networks of chemical reactions that are relevant to the experimental conditions that kinetic models must mirror, with every reaction requiring accurate kinetic data. Here we showcase Kinetica.jl, a new software package for constructing large-scale chemical reaction networks in a fully-automated fashion by exploring chemical reaction space with a kinetics-driven algorithm; coupled to efficient machine-learning models of activation energies for sampled elementary reactions, we show how this approach readily enables generation and kinetic characterization of networks containing $\sim10^{3}$ chemical species and $10^{4}$ - $10^{5}$ reactions. Symbolic-numeric modelling of the generated reaction networks is used to allow for flexible, efficient computation of kinetic profiles under experimentally-realizable conditions such as continuously-variable temperature regimes, enabling direct connection between bottom-up reaction networks and experimental observations. Highly efficient propagation of long-timescale kinetic profiles is required for automated reaction network refinement and is enabled here by a new discrete kinetic approximation. The resulting Kinetica.jl simulation package therefore enables automated generation, characterization, and long-timescale modelling of complex chemical reaction systems. We demonstrate this for hydrocarbon pyrolysis simulated over timescales of seconds, using transient temperature profiles representing those of tubular flow reactor experiments.
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