in which A is the pre-exponential factor, T refers to the temperature, m is the additional temperature exponent coefficient, E is the exponential temperature coefficient and R is the universal gas constant. The fitted Arrhenius equation is then used to regenerate the dataset at intervals of 5 K in temperature, which increases the size of the dataset and helps models learn about the temperature dependence of rate constants. To prevent over-fitting, the dataset is randomly divided into 90% as the training set and 10% as the validation set. The logarithms of rate constants are used in the training. Moreover, the reactions with only one or few recorded rate constants are devoted to assess the prediction accuracy.