A detailed understanding of reactive chemical systems would support the optimization of chemical processes through directed manipulations of promoting and interfering factors during the course of a reaction. For this purpose, we need to uncover the kinetic principles of a general (complex and noisy) chemical reaction network (CRN) from an electronic structure perspective. We will introduce KiNetX, a fully automated meta-algorithm for the kinetic modeling of general CRNs with rigorous uncertainty control. First, KiNetX identifies and eliminates all kinetically irrelevant species and elementary reactions to guide and, hence, accelerate the exploration of CRNs. Second, KiNetX propagates the correlated uncertainty in the network parameters (activation free energies or rate constants) to estimate the uncertainty of, e.g., product distributions or even entire reaction mechanisms. Third, KiNetX learns the sensitivity of species concentrations toward changes in individual network parameters based on Gaussian process regression, which allows us to systematically select the most efficient electronic structure model for each elementary reaction in a CRN given a predefined accuracy. We will present the theoretical details underlying KiNetX and demonstrate its individual steps by reference to various chemical scenarios.

Mechanism deduction from noisy chemical reaction networks

256th National Meeting (2018)

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