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.

中文翻译：

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使用机器学习预测与烷烃的羟基自由基反应的速率常数

羟基自由基与烷烃的夺氢反应在燃烧化学和大气化学中起着重要作用。然而，位点特异性反应常数很难通过实验和理论获得。最近，机器学习已经证明了其预测化学性质的能力。在这项工作中，开发了一种机器学习方法来预测标题反应的温度相关位点特定速率常数。多层神经网络 (NN) 模型是通过训练 11 个反应的位点特定速率常数而开发的，并设计了多种方案来提高预测精度。结果表明，所提出的神经网络模型在预测特定站点和整体速率常数方面是稳健的。