While earlier works lumped much of the complexity into one single layer , this work aims to develop a more strongly separated network architecture in the open-source deep learning framework PyTorch . This leads to the use of PyTorch's parameter groups that bundle similar parameters into distinct units and is highly beneficial, since manual handling of the larger number of parameters impedes interpretability. In other words, different parts of any generic microkinetic model get their own dedicated layers. This is also apparent from the grouping of stoichiometric coefficients, reaction orders and the parameters for the Arrhenius-and surface coverage dependency expressions in eqs. ( ) and ( ). This compartmentalization empowers efficient handling, interpretation and manipulation of those parameters. An overview of the proposed network architecture is depicted in fig. . This shows the general hCRNN structure as a parameterized function N (w l , b l , β) : (c, T ) → ṡi as well as the breakdown into dedicated layers. The single layer expressions resemble the terms in eq. ( ):