Mechanism Deduction from Noisy Chemical Reaction Networks

摘要

We introduce KINETX, a fully automated meta-algorithm for the kinetic analysis of complex chemical reaction networks derived from semiaccurate but efficient electronic structure calculations. It is designed to (i) accelerate the automated exploration of such networks and (ii) cope with model-inherent errors in electronic structure calculations on elementary reaction steps. We developed and implemented KINETX to possess three features. First, KINETX evaluates the kinetic relevance of every species in a (yet incomplete) reaction network to confine the search for new elementary reaction steps only to those species that are considered possibly relevant. Second, KINETX identifies and eliminates all kinetically irrelevant species and elementary reactions to reduce a complex network graph to a comprehensible mechanism. Third, KINETX estimates the sensitivity of species concentrations toward changes in individual rate constants (derived from relative free energies), which allows us to systematically select the most efficient electronic structure model for each elementary reaction given a predefined accuracy. The novelty of KINETX consists in the rigorous propagation of correlated free-energy uncertainty through all steps of our kinetic analyis. To examine the performance of KINETX, we developed AutoNetGen. It semirandomly generates chemistry-mimicking reaction networks by encoding chemical logic into their underlying graph structure. AutoNetGen allows us to consider a vast number of distinct chemistry-like scenarios and, hence, to discuss the importance of rigorous uncertainty propagation in a statistical context. Our results reveal that KINETX reliably supports the deduction of product ratios, dominant reaction pathways, and possibly other network properties from semiaccurate electronic structure data.