Data driven reaction mechanism estimation via transient kinetics and machine learning

Understanding the set of elementary steps and kinetics in each reaction is extremely valuable to make informed decisions about creating the next generation of catalytic materials. With structural and mechanistic complexities of industrial catalysts, it is critical to obtain kinetic information through experimental methods. As such, this work details a methodology based on the combination of transient rate/concentration dependencies and machine learning to measure the number of active sites, the individual rate constants, and gain insight into the mechanism under a complex set of elementary steps. This new methodology was applied to simulated transient responses to verify its ability to obtain correct estimates of the micro-kinetic coefficients. Furthermore, data from an experimental CO oxidation on a platinum catalyst was analyzed to reveal that Langmuir-Hinshelwood mechanism drives the reaction. As oxygen accumulated on the catalyst, a transition in the apparent kinetics was clearly defined in the machine learning analysis due to the large amount of kinetic information available from transient reaction techniques. This methodology is proposed as a new data driven approach to characterize how materials control complex reaction mechanisms relying exclusively on experimental data.

Automated summary

Understanding the elementary steps and kinetics in each reaction is crucial for decision-making about catalytic materials. This work introduces a methodology combining transient rate/concentration dependencies and machine learning to measure active sites, rate constants, and investigate reaction mechanisms under complex steps. The approach can provide accurate estimates of micro-kinetic coefficients and reveal how materials control reaction mechanisms through experimental data.