To improve the neural ODE models, the kinetic and thermodynamic information about CO2 hydrogenation to methane was added into the architecture of neural ODE model. From the lower row of plots in Figure one can see that the suggested approach improves the predicting ability of ANN model significantly. With this model, the value of the loss function for the test data set has decreased around 2900 times compared to the baseline model (Table ). Moreover, the KCNODE model can describe the data outside of the training range correctly that is seen from the presented results. It is also worth noting that the loss function for the training data set in the case of the KCNODE model is lower than in the case of the baseline model (see Table ). Wherein, the numbers of learnable parameters for the baseline and KCNODE models are 177 and 70, respectively. Thus, an increased number of parameters of the baseline model is not enough to achieve the same low value of the loss function for training data set as in the case of the KCNODE model. It is obvious that an increase in number of parameters for the baseline model will lead only to a further increase in the loss function value of the test data set. It is well-known that real data has experimental errors that can influence the performance of ANN models. This can be especially critical in the case of small data sets. For investigating this influence, KCNODE models were obtained by using the training data sets with added Gauss noise (0-10 %). The loss function values for the models trained on data of various noise levels are presented in the Table (training of the models in Figure , parity plots can be found in Figure ). One can see an increase in the values of the loss function for training and test data sets with increasing noise in the training data, but the generalization of the obtained models is still good (see Figure ). It is interesting to mention that the obtained KCNODE models are quite resistant to overfitting. This cannot at all be said about the baseline model. To investigate the resistance of KCNODE model to overfitting in more detail, the model with an increased number of learnable parameters (the number of nodes in the hidden layer was increased from 8 to 20) was trained by using the same training data set (see Table ). The obtained model has also high predicting ability. Wherein, the number of parameters of the obtained ANN model is 166 and even higher than the number of independent experimental data points (the number of reaction conditions × the number of points of V × the number of reactions = 8•7•2 = 112 < 166). It proves that the suggested architecture of ANN is resistant to overfitting.