  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 physical and mechanistic complexity 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,
experimental CO oxidation data was analyzed to reveal the Langmuir-Hinshelwood
mechanism driving the reaction. As oxygen accumulated on the catalyst, a
transition in the mechanism 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.
