To ensure stoichiometric consistency splitting of forward and backward reactions into two separate sub-layers according to Barwey and Raman is employed. By using the notion that ν + i,j = -ν - i,j and assuming elementary steps for all surface, adsorption, and desorption reactions (i.e. ν i,j = -ν i,j for reactants and ν i,j = 0 for products) one can derive a formulation relying only on the stoichiometric coefficients of the forward reactions to accurately describe both the reaction orders and the stoichiometry of forward and backward reactions at the same time. To our knowledge the proposed extension is novel to the CRNN methodology and extends usability to equilibrium reactions without doubling the amount of network parameters by singularly including both the forward and backward reaction. These common and well documented assumptions aid in reducing the number of trainable parameters in the power-law and stoichiometry layer from 4 (m × n) for the fully explicit notation down to (m × n) for the elementary step notation. During preliminary testing, this splitting has significantly sped up training and increased interpretability of the obtained results for reaction systems as small as m = 2 and n = 2. Recently, attempts have been made to further decrease the number of parameters by using the fact that atoms have to be conserved during any reaction . Examining this methodology is however, not the focus of this work. This construction leaves only one major hyperparameter up to the users choice: The number of neurons in each layer (indicated by the index j) which corresponds directly to the number of elementary reactions sought for in the microkinetic model . Thus, even this a priori undetermined hyperparameter gains physical interpretability and can be used to reason about the reaction system at hand. Note that generally hyperparameters are not available through training but have to be chosen beforehand according to some heuristics. In our specific case, an educated guess towards the correct choice of the number of neurons can be made by prior insight into or literature knowledge about the investigated system.