A Chemical Space Perspective on Diastereomeric Barriers in Alkylperoxy-to-Hydroperoxyalkyl Isomerization

TL;DR

This study demonstrates the significant impact of stereochemistry on low-temperature hydrocarbon autooxidation pathways, providing a large dataset and insights for improved reaction modeling.

Contribution

It offers the first large-scale density-functional-theory dataset analyzing stereochemical effects on alkylperoxy to hydroperoxyalkyl isomerization.

Findings

Diastereomeric pathways can differ by over 60 kcal/mol due to steric strain.

Explicit stereochemical treatment reveals non-degenerate reaction pathways.

Stereochemistry influences the kinetic relevance of reactive channels.

Abstract

Low-temperature hydrocarbon autooxidation involves radical intermediates whose reactivity depends not only on the stereochemistry of the intermediates themselves, but also on that of the transient species encountered along the reaction path. This study offers large-scale evidence for the importance of stereochemistry in low-temperature autooxidation by propagating stereochemical information from 498 C1-C7 hydrocarbons through radical formation, $\mathrm{O_2}$ addition, and the isomerization of alkylperoxy ROO radicals to hydroperoxyalkyl QOOH radicals. The resulting dataset comprises density-functional-theory-level data for 5,356 species, including 2,324 cyclic diastereomeric transition states associated with 1,162 unique ROO -> QOOH isomerization reactions, with transition-state connectivity confirmed by intrinsic reaction coordinate analysis. Explicit stereochemical treatment reveals that diastereomeric pathways may be either degenerate or separated by more than 60 kcal/mol, with the magnitude of these differences governed by steric strain at the carbon bearing the peroxyl group. These results show that constitutionally collapsed molecular representations can systematically miss kinetically relevant reactive channels and provide a foundation for stereochemistry-aware mechanism generation, rate estimation, and predictive combustion modeling.