The FNN model often converges slowly due to the error backpropagation algorithm, but a good weight initialization scheme can overcome this issue to some extent. To take advantages of both ANN and decision tree-based algorithms, a hybrid model, namely XGB-FNN, was designed in our previous work. In the XGB- The FNN model is constructed with an architecture of 1-3-1, i. e., one input layer, three hidden layers, and one output layer, using the PyTorch framework (version 1.4.0). The XGB model is built by XGBoost python package (version 0.80) under the scikit-learn framework (version 0.20.2). The hyperparameters, such as the number of hidden neurons and the learning rate, have to be tested before training. Grid search is used to choose suitable hyperparameters. To assess the accuracy and robustness of the machine learning models, The Leave-One-Out (LOO) cross validations are firstly applied. The "one" left out in the cross validations denotes the rate constants of a reaction at different temperatures，which are taken as the test set. The rate constants of the remaining n-1 reactions are randomly divided into a training set and a validation set by a ratio of 9:1. Therefore, there are three datasets in the LOO cross validations: training, validation and test. For simplicity, the dataset group is labeled as "DGLOO".