Kinetics of N2 Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study

16 August 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Diazo compounds are commonly employed as carbene precursors in carbene transfer reactions during a variety of functionalization procedures. Release of N2 gas from diazo compounds may lead to carbene formation, and the ease of this dissociation is highly dependent on the characteristics of the substituents located in the vicinity of the diazo moiety. A quantum mechanical density functional theory assisted by machine learning was used to investigate the relationship between the chemical features of diazo compounds and the activation energy required for the N2 dissociation. Our results suggest that diazo molecules possessing a higher positive partial charge on the carbene carbon and more negative charge on the terminal nitrogen, encounter a lower energy barrier. A more positive C charge decreases the π-donor ability of the carbene lone pair to the π∗ orbital of N2 , while the more negative N charge is a result of a weak interaction between N2 lone pair and vacant p orbital of the carbene. The findings of this study can pave the way for molecular engineering for the purpose of carbene generation which serves as a crucial intermediate for many chemical transformations in synthetic chemistry.

Keywords

Diazo compounds
Machine Learning
Density Functional Theory
Kinetics of N2 Release

Supplementary materials

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Title
Kinetics of N2 Release from Diazo Compounds: A Combined Machine Learning-Density Functional Theory Study
Description
SI
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