  The temporal analysis of products (TAP) technique produces extensive
transient kinetic data sets, but it is challenging to translate the large
quantity of raw data into physically interpretable kinetic models, largely due
to the computational scaling of existing numerical methods for fitting TAP
data. In this work, we utilize kinetics-informed neural networks (KINNs), which
are artificial feedforward neural networks designed to solve ordinary
differential equations constrained by micro-kinetic models, to model the TAP
data. We demonstrate that, under the assumption that all concentrations are
known in the thin catalyst zone, KINNs can simultaneously fit the transient
data, retrieve the kinetic model parameters, and interpolate unseen pulse
behavior for multi-pulse experiments. We further demonstrate that, by modifying
the loss function, KINNs maintain these capabilities even when precise
thin-zone information is unavailable, as would be the case with real
experimental TAP data. We also compare the approach to existing optimization
techniques, which reveals improved noise tolerance and performance in
extracting kinetic parameters. The KINNs approach offers an efficient
alternative for TAP analysis and can assist in interpreting transient kinetics
in complex systems over long timescales.
