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 .
We introduce KiNetX, a fully automated meta-algorithm for the kinetic analysis of complex chemical reaction networks derived from semi-accurate 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 assess 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 semi-accurate electronic structure data .
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simulation and analysis of general (complex and noisy)
analysis of complex
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with rigorous uncertainty control
derived from semi-accurate but efficient electronic structure calculations
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cope with method inherent errors in quantum chemical
(i) accelerate the automated exploration of such networks, and (ii) cope with model-inherent errors in electronic structure
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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
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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
reduce a complex network graph to a comprehensible mechanism
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activation free energies
rate constants (derived from relative free energies)
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quantum chemical
electronic structure
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For a rigorous analysis of the KiNetX algorithm
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
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a random generator of artificial reaction networks , AutoNetGen, which encodes
AutoNetGen. It semirandomly generates chemistry-mimicking reaction networks by encoding
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chemical scenarios which is necessary to investigate the reliability and efficiency of KiNetX
chemistry-like scenarios and, hence, to discuss assess the importance of rigorous uncertainty propagation
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reliable mechanism deduction from noisy chemical reaction networks is feasible through the combination of first-principles calculations, kinetic-modeling techniques, and rigorous statistical methods
KiNetX reliably supports the deduction of product ratios, dominant reaction pathways, and possibly other network properties from semi-accurate electronic structure data