The preparation of meaningful training data is of utmost importance for the training success of any machine learning project including physically motivated aNNs . It has to be sampled from a wide yet physically feasible field of possible conditions. The data collection will proof to influence training success heavily, as is reported in literature . In context of the here presented hCRNNs the goal is to predict species formation rates, ṡi , from data containing gas and surface phase compositions, c G i and c S i (or simply c i in short), and temperature, T . The latter three are hereafter referred to as a state and directly sampled while the formation rates are state-dependent. The sampling strategy is schematically depicted in fig. From a range of possible states a number of points is chosen uniformly . Those states are likely to be distant from equilibrium or steady-state (compare trajectories in fig. ) and thus show strongly kinetic behavior. The proposed sampling strategy is only concerned with the initial reaction rates at these conditions and contain no temporal information about the overall progression towards steady-state. This kind of data is referred to as being differential for it does not contain any information about its history. We refer to this setup as the maximally transient regime because it allows for observation of kinetic effects far from steady-state. Specifically, a two phase system containing a catalytic surface in contact with a gas phase is considered and studied. Transport limitations are neglected. For this system, a single system state is generated by sampling gas and surface phase compositions (x i and Θ i ) according to a flat Dirichlet distribution . Concentration measures are subsequently derived from the surface site density, Γ 0 , and the gas phase pressure, p, and temperature, T , according to