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.
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http://dx.doi.org/10.1021/acs.jcim.1c00809 | DOI Listing |
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