In this part we investigated the generalization of the neural ODE models trained by using the small, simulated data set. The process of training the neural ODE models represented as dependency between loss function and epoch number is summarized in the Figures S1. The One can see that the baseline model fits the training data correctly which corresponds to the low value of loss function (see Table ). However, this model performs purely when it was used for predicting and describing data outside of the training range. This is clearly visible in Figure ) where the baseline model was used for extrapolating and fitting the test data set. Accordingly, a high value for the loss function was obtained for the test data test (Table ). ANN models are known to require a lot of data for training, and the obtained result proves this. Therefore, we can conclude that the standard approach for chemical kinetics modelling using neural ODE cannot be applied when ANN models are trained based on small data set.