The above discussed strategy for setting up a training workflow can be used to gradually increase model complexity and maximize training process. Although the demonstration of the introduced method for a generic yet universal heterogeneous mechanism suggests the potential for extension to all elementary step reaction mechanisms, it is evident that interpretation of results for complex mechanisms requires a detailed analysis. Despite the promise of easy interpretation of CRNNs, this work also shows that discovery for complex cases is not trivial and still requires substantial domain knowledge. Nonetheless, it was demonstrated that kinetic discovery for heterogeneous mechanisms is possible and can yield good approximations for pre-exponential constants, activation energies and even coverage dependency parameters. The so discovered kinetic model obeys thermodynamic and kinetic restrictions without any prior knowledge of the mechanism. In it's current state, the hCRNN only fulfills these restrictions by training, i.e., thermodynamically and kinetically consistent data must be supplied during training. For example, the conservation of mass is not enforced by the network construction but implicitly satisfied by observation of mass conserving processes. The conservation of mass for the retained mechanism can be checked after the training, but requires the stoichiometric compositions of the observed species. Similar reasoning applies to thermodynamic consistency: Although reversibility is assured to be captured when observed, the correctness of equilibrium constants depends on the presence of correct equilibria in the training data. From this, it is reasonable to assume that training data plays a critical role in interpreting the results of the method. This work is only concerned with the maximally transient regime because it is conceptually interesting. However, the authors acknowledge that this type of data is difficult to access experimentally for a few reasons: Firstly, extremely fast, yet still precise analytics would be needed to correctly resolve the formation and consumption dynamics in maximally transient regime. Furthermore, since the only way to reliably load a catalytic surface is by bringing it into contact with a gas phase and advancing it towards or even into chemical equilibrium, the start-up of such a reactor would be tedious and skewed towards coverages close to equilibrium. Lastly, even if analytics and experimental setup were ideal, instrumentation is oftentimes not: switching times of valves and nonideal residence time distributions in pipes make highly transient kinetic measurements experimentally hard to realize. Nonetheless, the described sampling method was specifically designed to elucidate the dynamic processes far from equilibrium. Indeed, we can conclude that the fastest (dominating) kinetic behavior is well recovered under all circumstances by the hCRNN in the chosen maximally transient regime. If the mechanism contains very fast steps, these will be identified in favor of slower ones all the while dynamic behavior is well retained. The number of reactions needed for describing the kinetic behavior depends on their speed (processes that dominate the overall kinetics are captured while additional slow ones may not be correctly identified and consequently omitted from the construction). Thus, best kinetic discovery is possible, if all reactions are equally fast. This in terms means that slow processes are not observable through the chosen sampling strategy. In contrast, in a steady-state experiment, the slowest step limits productivity and dictates overall reactor behavior. Thus, in a typical steady-state catalytic experiment, only the slowest reaction can be observed, while all the others are (assumed to be) in quasi-equilibrium. This can be formalized with the concept of the RDS. In that regard, the observability of fast reactions in the maximally transient regime is the counterpart of the observability of rate determining reactions in a steady-state experiment. Full kinetic discovery by hCRNNs might consequently be achievable by observing different regimes: While this work demonstrates that the use of maximally transient data favors identification of fast reactions, theory promises the discovery of slow reactions from steady-state data. Whether or not a well-chosen sampling strategy is capable of exposing both fast and slow reactions to the hCRNN remains unclear up to now, and answering this question is left as part for future work. Such a strategy might be based on observing varying time horizons of a reaction system evolving from its initial state into steady-state or equilibrium. This might mean observing the reaction system at different residence times (for continuous systems) or physical times (for transient systems) in the systematic way detailed throughout this work. This kind of experimentation, in contrast to maximally transient experiments, is achievable by, e.g., the periodic transient kinetics method (PTK) and has been demonstrated before . Ultimately, understanding the behavior of hCRNNs in the different rate regimes might allow for the design of a training strategy tailored to the reaction system at hand and consequently enable kinetic discovery even for heterogeneous systems.