Predicting Rate Constants of Hydrogen Abstraction Reactions between OH/HO₂ and Alkanes by Machine Learning Models

The hydrogen abstraction reactions by small radicals from fuel molecules play an important role in the oxidation of fuels. However, experimental measurements and/or theoretical calculations of their rate constants under combustion conditions are very challenging due to their high reactivity. Machine learning offers a promising approach to predicting thermal rate constants. In this work, three machine learning methods, XGB, FNN, and XGB-FNN hybrid algorithms, were employed to train and predict the rate constants of the hydrogen abstraction reactions between alkanes and OH/HO₂. Six descriptors were selected according to the Pearson correlation coefficients, the importance of descriptors, and the clustering heat map. It was proven that the XGB-FNN model is the most robust. The constructed XGB-FNN model achieved an average deviation of 89.13% for the alkanes + OH reactions and 190.93% for the alkanes + HO₂ reactions on their respective prediction sets. The model was also used to predict the rate constants of the reactions involving larger alkanes, demonstrating its extrapolation capability. Furthermore, the model has the ability to distinguish the reactivity of the reactions with the hydrogen atom abstracted at different sites of alkane.