Unimolecular Dissociation of γ-Ketohydroperoxide via Direct Chemical Dynamics Simulations

Abstract

γ-Ketohydroperoxide [3-(hydroperoxy)propanal] is an important reagent in synthetic chemistry and, in particular, oxidation reactions. It is considered to be a precursor for secondary organic aerosol formation in the troposphere. Due to enhanced reactivity and limitations associated with analytical techniques, theoretical methods have been employed to study the unimolecular reactivity of hydroperoxides. A number of automated reaction discovery techniques have been used to study the reactivity of γ-ketohydroperoxide, and a large number of reactions have been reported in such studies. In the present work, we have investigated the unimolecular reaction dynamics of this molecule using electronic structure theory calculations and direct chemical dynamics simulations to assess the relevance of different reaction pathways. Classical trajectories were launched from the reactant well with fixed amounts of total energies and integrated on-the-fly using density functional B3LYP/6-31+G* model chemistry. Three dissociation channels among the previously reported reactions were identified as important. Korcek decomposition, which was proposed earlier as a source of carbonyl compounds from thermal decomposition of γ-ketohydroperoxide, was not observed in the present high-temperature simulations. However, trajectories showed the formation of carbonyl compounds such as aldehydes via other pathways. Results are compared with previous studies, and detailed atomic-level reaction mechanisms are presented.

Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces

“…There are potentially nonstatistical effects in the decomposition of 3HPP, as was shown by Naz and Paranjothy, who started trajectories from the 3HPP well. Goldsmith et al also investigated dynamics effects by starting trajectories from the FOSP that forms 3HPP in the oxidation system.…”

“…Goldsmith et al 121 used high-level theory coupled with sophisticated transitionstate theory and a master equation system to arrive at rate coefficients. The other study, by Naz and Paranjothy, 122 focused more on the dynamics of this system.…”

Chemical Networks from Scratch with Reaction Prediction and Kinetics-Guided Exploration

“…Reaction prediction methods with minimal heuristic guidance have recently achieved qualitative improvements in accuracy, cost, and throughput that make predicting relatively short reaction sequences involving small molecules routine in many scenarios. [1][2][3][4][5][6][7][8][9][10][11] Although emerging strategies vary in detail, they all ultimately rely on characterizing the transition states of prospective reactions to determine reaction outcomes. In this, the field as a whole has benefited from new low-cost potential energy surfaces, [12][13][14] double-ended algorithm refinement including string and band methods, [15][16][17][18] and ongoing developments in machine learning (ML).…”