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

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Physical Chemistry

Background:

• Diazo compounds are essential precursors for generating carbenes, reactive intermediates widely used in synthetic chemistry for various functionalization reactions.
• The efficiency of carbene generation from diazo compounds is critically influenced by the electronic and structural properties of substituents surrounding the diazo group.

Purpose of the Study:

• To investigate the relationship between the molecular features of diazo compounds and the activation energy required for nitrogen gas (N₂) elimination.
• To identify key electronic properties that facilitate or hinder carbene formation via N₂ release.

Main Methods:

• Employed a computational approach combining quantum mechanical density functional theory (DFT) with machine learning (ML) techniques.
• Analyzed the correlation between partial charges on specific atoms within diazo compounds and the calculated energy barriers for N₂ elimination.

Main Results:

• Identified that diazo compounds with a higher positive partial charge on the carbene carbon and a more negative charge on the terminal nitrogen exhibit a lower energy barrier for N₂ release.
• Explained that a more positive carbene carbon reduces the π-donor ability, while a more negative terminal nitrogen indicates weaker interactions within the N₂ molecule, both facilitating decomposition.

Conclusions:

• The electronic distribution, specifically partial charges, significantly dictates the ease of carbene generation from diazo compounds.
• Findings provide a basis for the rational molecular design of diazo compounds for controlled carbene generation in synthetic applications.