The above assessments demonstrate that the performance of the three models using similarity as one of the descriptors is comparable to the corresponding activation energy-based models. However, in sharp contrast to the activation energy, the similarity can be easily generated from the reactant and product fingerprints and thus doesn't require extra quantum chemistry calculations, endowing great potential in practical applications. By comparing the three machine learning models that use similarity as a descriptor, the XGB-FNN model achieves a good balance between the accuracy and the stability in prediction. In addition, the predicted rate constant by the XGB model may exhibit discontinuous behavior with temperature, as shown in Fig. for the reaction between n-C8H18 and CH3 as an example. By contrast, the predicted rate constant by the XGB-FNN model has a relatively smooth temperature dependence. Therefore, the rate constants will be predicted by the XGB-FNN model with similarity as a descriptor hereafter.