Larger range of robustness may be necessary for some applications. An easy way to increase this range is to add simulations to the training set. For example, we trained a new model that uses two simulations per temperature between 3,100 K and 3,600 K in the training data and we call it the "12 MD" KMC model. For the "12 MD" KMC model, the range where the predictions can be con-2500 3000 3500 4000 4500 Temperature (K) sidered as robust increase by around 400 K (see Fig. ). . One possible venue for further optimization of the kinetic model is to enhance the of the training data. For example, in Fig. , the robustness of H 2 is the one that degrades the fastest with temperature. This degradation is due to the lower number of reactions involving this molecule. The atomic features of the hydrogens of H 2 are specific to this molecule, so the number of total reactions involving these atomic features is smaller. This implies a higher 95% CI on these reactions and a less robust extrapolation to new temperatures. This effect can be controlled by adding more reactions involving H 2 to the training data. For example, adding an MD simulation starting partly with H 2 to the training data should further improve the range of robustness of the model over another simulation starting with C 4 H 10 . Another choice would be to increase the temperature training range; this could allow to have robust predictions down to lower temperature. The optimization in the choice of training data would reduce the number of training simulations and increase the range of good robustness of the model.