Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH3/H/O Radicals

Abstract

Accurate determination of reaction rate constants in the combustion circumstance is very challenging both experimentally and theoretically. In this work, three supervised machine learning algorithms, including XGB, FNN and XGB-FNN, are used to develop quantitative structure−property relationship models for the estimation of the rate constants of hydrogen abstraction reactions from alkanes by the free radicals CH3, H and O. The molecular similarity based on Morgan molecular fingerprints combined with the topological indices are proposed to represent chemical reactions in the machine learning models. Using the newly constructed descriptors, the performance of each algorithm in prediction was found to be comparable and even superior to the corresponding one using the activation energy as a descriptor. The use of activation energy as a descriptor has previously been shown to significantly improve prediction accuracy (Fuel, 2022, 322, 124150) but typically requires cumbersome ab initio calculations. The hybrid XGB-FNN algorithm performed better than the other two algorithms, which could reasonably predict reaction rate constants of hydrogen abstractions from different sites of alkanes and their isomers, indicating a good generalization ability. It is expected that the reaction descriptors proposed in this work can be applied to build machine leaning models for other reactions.