Mechanism Deduction from Noisy Chemical Reaction Networks

We introduce KiNetX, a fully automated meta-algorithm for the kinetic simulation and analysis of general (complex and noisy) chemical reaction networks with rigorous uncertainty control. It is designed to cope with method inherent errors in quantum chemical calculations on elementary reaction steps. We developed and implemented KiNetX to possess three features. First, KiNetX identifies and eliminates all kinetically irrelevant species and elementary reactions to guide, and thereby, accelerate the exploration of chemical reaction space. Second, KiNetX propagates the correlated uncertainty in the network parameters (activation free energies) obtained from ensembles of quantum chemical models to infer the uncertainty of product distributions and entire reaction mechanisms. Third, KiNetX estimates the sensitivity of species concentrations toward changes in individual activation free energies, which allows us to systematically select the most efficient quantum chemical model for each elementary reaction given a predefined accuracy. For a rigorous analysis of the KiNetX algorithm, we developed a random generator of artificial reaction networks, AutoNetGen, which encodes chemical logic into their underlying graph structure. AutoNetGen allows us to consider a vast number of distinct chemical scenarios which is necessary to investigate the reliability and efficiency of KiNetX in a statistical context. Our results reveal that reliable mechanism deduction from noisy chemical reaction networks is feasible through the combination of first-principles calculations, kinetic-modeling techniques, and rigorous statistical methods. ∗corresponding author: [email protected] 1 ar X iv :1 80 3. 09 34 6v 1 [ ph ys ic s. ch em -p h] 2 5 M ar 2 01 8