A scarcity of known chemical kinetic parameters leads to the use of many reaction rate estimates, which are not always sufficiently accurate, in the construction of detailed kinetic models. To reduce the reliance on these estimates and improve the accuracy of predictive kinetic models, we have developed a high-throughput, fully automated, reaction rate calculation method, AutoTST. The algorithm integrates automated saddle-point geometry search methods and a canonical transition state theory kinetics calculator. The automatically calculated reaction rates compare favorably to existing estimated rates. Comparison against high level theoretical calculations show the new automated method performs better than rate estimates when the estimate is made by a poor analogy. The method will improve by accounting for internal rotor contributions and by improving methods to determine molecular symmetry.

Completely automated mechanism generation of detailed kinetic models is within reach in the coming decade. The recent developments in this field of chemical reaction engineering are anticipated to lead to some groundbreaking discoveries in the future, extending our fundamental understanding and resolving many of today's society problems such as energy production and conversion, emission reduction, greener chemical production processes, etc. In the present review, the focus is on the core of these automated mechanism generation for gas‐phase and solution‐phase processes that is on how the reaction kinetics and thermodynamic and transport properties of species are estimated and calculated starting from the fundamental elements of the software. With tasks such as the definition of reaction rules and reaction families, the unambiguous representation of species, and the choice of different termination criteria, generating a good reaction mechanism is still not as simple as pressing a “run” button. One of the main challenges that still needs to be overcome is how to deal with data scarcity and the combination with affordable computational chemistry calculations seems the logical step forward. The best practices are illustrated in a butane pyrolysis case study, which also exposes the challenges in the field of automatic kinetic model generation.

Detailed kinetic models to aid the understanding of complex chemical systems require many thousands of reaction rate coefficients, most of which are estimated, some quite approximately and with unknown uncertainties. This motivates the development of high-throughput methods to determine rate coefficients via transition state theory calculations, which requires the automatic prediction of transition state (TS) geometries. We demonstrate a novel approach to predict TS geometries using a group-additive method. Distances between reactive atoms at the TS are estimated using molecular group values, with the 3D geometry of the TS being constructed by distance geometry. The estimate is then optimized using electronic structure theory and validated using intrinsic reaction coordinate calculations, completing the fully automatic algorithm to locate TS geometries. The methods were tested using a diisopropyl ketone combustion model containing 1393 hydrogen abstraction reactions, of which transition states were found for 907 over two iterations of the algorithm. With sufficient training data, molecular group contributions were shown to successfully predict the reaction center distances of transition states with root-mean-squared errors of only 0.04 Å.

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