Predicting Rate Constants of Hydroxyl Radical Reactions with Alkanes Using Machine Learning

The hydrogen abstraction reactions of the hydroxyl radical with alkanes play an important role in combustion chemistry and atmospheric chemistry. However, site-specific reaction constants are difficult to obtain experimentally and theoretically. Recently, machine learning has proved its ability to predict chemical properties. In this work, a machine learning approach is developed to predict the temperature-dependent site-specific rate constants of the title reactions. Multilayered neural network (NN) models are developed by training the site-specific rate constants of 11 reactions, and several schemes are designed to improve the prediction accuracy. The results show that the proposed NN models are robust in predicting the site-specific and overall rate constants.

Automated summary

The study developed a machine learning approach to predict the temperature-dependent site-specific rate constants of hydrogen abstraction reactions, demonstrating robustness in accurately predicting the site-specific and overall rate constants.