Automation of chemical kinetics: Status and challenges

Abstract

Driven by synergic advancements in high performance computing and theory, the capability to estimate rate constants from first principles has evolved considerably recently. When this knowledge is coupled with a procedure to determine a list of all reactions relevant to describe the evolution of a reacting system, it becomes possible to envision a methodology to predict theoretically the reaction kinetics. However, if a thorough examination of all possible reaction channels is desired, the number of reactions for which a rate constant estimate is needed can become quite large. This determines the need for rate constant estimation automation. In the present work, the status of this rapidly evolving field is reviewed, with emphasis on recent advancements and present challenges. Thermochemistry is the field where automation is most advanced. Entropies, heat capacities, and enthalpies can be determined efficiently with accuracy comparable to experiments for most chemical species containing a limited number of atoms, while machine learning can be used to improve the computational predictions for large chemical species using reduced computational resources. Several approaches have been proposed to automatically investigate the reactivity over complex potential energy surfaces, while rate constants for elementary steps can be determined accurately for several reaction classes, such as abstraction, addition, beta-scission, and isomerization. Kinetic mechanisms can be automatically generated using methodologies that differ for level of complexity and required physical insight. Among the challenges that are still to be met are the estimation of rate constants for intrinsically multireference reaction classes, such as barrierless processes, the containment of the number of reactions to screen in mechanism development, and the integration of the existing automated software. It is suggested that the synergy between experiment and theory should evolve towards a stage where experiments are focused on the estimation of parameters where theoretical tools are least predictive, and vice versa.