Calculating the thermal rate constants of elementary combustion reactions is of great importance in theoretical chemistry. Machine learning has become a powerful, data-driven method for predicting rate constants nowadays. Recently, the molecular similarity combined with the topological indices were proposed to represent the hydrogen abstraction reactions of alkane [J. Chem. Inf. Model. 2023, 63, 5097–5106], which are, however, not applicable to alkane cracking reactions, another important class of combustion reactions, due to the cleavage of the C–C bond. In this work, a new feature selection scheme is proposed to describe both bimolecular and unimolecular cracking reactions. Molecular descriptors are elaborately selected individually for both reactants and products from those generated by the open-source software RDKit. Machine learning models combined with these molecular descriptors are proven to have the ability to accurately predict rate constants of both the hydrogen abstraction reactions of alkanes by CH₃ and the alkane cracking reactions. The average deviation of the XGB-FNN model for prediction is around 60% for the hydrogen abstraction reactions of alkanes and 100% for the alkane cracking reactions. It is expected that the descriptors proposed in this work can be applied to build machine learning models for other reactions.

中文翻译：

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使用机器学习预测烷烃裂解反应的速率常数

计算基本燃烧反应的热速率常数在理论化学中非常重要。如今，机器学习已成为一种强大的、数据驱动的预测速率常数的方法。最近，提出了分子相似性与拓扑指数相结合来表示烷烃的夺氢反应[ J. Chem.信息。模型。2023 , 63 , 5097–5106]，然而，由于 C-C 键的断裂，它们不适用于另一类重要的燃烧反应——烷烃裂解反应。在这项工作中，提出了一种新的特征选择方案来描述双分子和单分子裂解反应。分子描述符是从开源软件 RDKit 生成的反应物和产物中单独精心选择的。机器学习模型与这些分子描述符相结合被证明能够准确预测CH ₃烷烃夺氢反应和烷烃裂解反应的速率常数。 XGB-FNN 模型预测烷烃夺氢反应的平均偏差约为 60%，烷烃裂解反应的平均偏差为 100%。预计这项工作中提出的描述符可以应用于构建其他反应的机器学习模型。