Development of Predictive Models of the Kinetics of a Hydrogen Abstraction Reaction Combining Quantum-Mechanical Calculations and Experimental Data

Predicting reactivity dynamics of halogen species and trace organic contaminants using machine learning models

Reactions of reactive halogen species (Cl•, Br•, and Cl2•-) with trace organic contaminants (TrOCs) have received much attention in recent years, and their k values are fundamental parameters for understanding their reaction mechanisms. However, k values are usually unknown. In this study, we developed machine learning (ML)-based quantitative structure-activity relationship (QSAR) models to predict k values. We tested five algorithms, namely, random forest, neural network, XGBoost, support vector machine (SVM), and multilinear regression, using molecular descriptors (MDs) and molecular fingerprints (MFs) as inputs. The optimal algorithms were MD-XGBoost for Cl• and Br•, and MF-SVM for Cl2•-, respectively, with R2test values of 0.876, 0.743, and 0.853. We found that electron-withdrawing/donating groups tended to interfere with the reactivity of Cl2•- more than Cl• and Br•. This explains why MFs are better inputs for predictive models of Cl2•-, whereas MDs are more suitable for Cl• and Br•. Furthermore, we interpreted the models using SHAP analysis, and the results indicated that our models accurately predicted k values both statistically and mechanistically. Our models provide useful tools for obtaining unknown k values and help researchers understand the inherent relationships between the models.

Mechanism, kinetics and selectivity of a Williamson ether synthesis: elucidation under different reaction conditions

New mechanistic understanding and the quantification of reaction kinetics shed light on the large impact of the solvent on selectivity.

Theoretical Studies of the Hydrogen Abstraction from Poly(oxymethylene) Dimethyl Ethers by O2 in Relation with Cetane Number Data

Poly(oxymethylene) dimethyl ethers (PODME _n , n = 2-6) are novel oxygenated compounds that can be used as promising candidates for new-generation fuels because of their excellent combustion performance. The oxidation of PODME _n (n = 2-6) is essential for the understanding of the combustion process. It is necessary to study the relationship between kinetic parameters and cetane number (CN) of PODME _n (n = 2-6). In order to predict initiation step rate constants for the oxidation of PODME _n (n = 2-6), quantum mechanical calculations are performed using M06-2X/6-311G(d,p) and B3LYP/6-311G(d,p) methods. Structural, energetic, thermodynamics, and kinetics of the automatic ignition process are assessed. The kinetic model based on the conventional transition state theory is used to calculate the initiation step reaction rate constants at 1000 K. In both M06-2X/6-311G(d,p) and B3LYP/6-311G(d,p) methods, the calculated rate constants of the hydrogen abstraction process have an excellent correlation with the experimental CN of PODME _n (n = 2-6). Our methodology presented here can be used to simulate chemical kinetics for other fuel additives.