Accelerating Reaction Discovery through AI-HTE Integration: Nitrene-Mediated C-O Coupling as a Validated Case Study.

Area of Science:

• Organic Chemistry
• Catalysis
• Synthetic Methodology

Background:

• Carbamates are vital in pharmaceuticals, agrochemicals, and materials.
• Existing carbamate synthesis often involves hazardous isocyanates or lacks functional group tolerance.

Purpose of the Study:

• To develop an efficient, isocyanate-free method for carbamate synthesis.
• To establish a versatile catalytic platform using dioxazolones for carbamate formation.
• To integrate machine learning for optimizing synthetic reactions.

Main Methods:

• Ruthenium-catalyzed C-O coupling of dioxazolones with phenols and alcohols.
• High-throughput reaction screening integrated with machine learning.
• Mechanistic studies including DFT calculations.

Main Results:

• Efficient carbamate synthesis under mild conditions with CO2 as the sole byproduct.
• Broad substrate scope and excellent functional group tolerance.
• Successful late-stage functionalization and gram-scale synthesis.
• Machine learning model accurately predicted reaction yields for new substrates.

Conclusions:

• Dioxazolone-derived nitrenes provide a general platform for carbamate synthesis.
• The developed method is practical, scalable, and amenable to late-stage functionalization.
• Integrating data-driven tools like machine learning accelerates synthetic methodology development.