Journal
JOURNAL OF CHEMICAL INFORMATION AND MODELING
Volume 61, Issue 9, Pages 4259-4265Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.jcim.1c00809
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Funding
- National Natural Science Foundation of China [21773297, 21973108, 21973109, 21921004]
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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.
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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