The CRNN formalism introduced by Ji and Deng takes advantage of the superior parameter fitting capabilities of artificial neural networks (aNNs) for identifying kinetic constants. The underlying structure makes use of the similarity between the parameterization of the rate constant eq. ( ) in log-scale, the stoichiometric relation eq. ( ) and the general mathematical formulation of a single layer in multilayer aNNs. By construction, CRNNs obey the above mentioned well-known physicochemical laws of chemical kinetics for homogeneous reaction systems. This not only guarantees consistency with physical intuition but also ensures interpretability of the network discovered constants. As for most known machine learning algorithms, results are obtained by training -the process of fitting the network parameters to match the network output to supplied training data. The sufficiently trained CRNN is a ready to use, drop-in replacement for conventional kinetic expressions able to reproduce kinetic behavior and yields not only the usable kinetic expression but also guesses for the kinetic parameters. Applications of this concept have been described for a plethora of nontrivial reaction networks in conjunction with different reactor and analysis setups with great success . Further improvements upon the neural network enabled differential equation solver allows to couple the results to the reactor scale more robustly . In terms of network construction, parameter reduction was achieved by introduction of mass conserving stoichiometry layers through identifying the null space of the stoichiometric matrix . The use of Bayesian inference has been demonstrated to further allow for uncertainty quantification of the retained parameters .