Accelerating the discovery of multicatalytic cooperativity.

Area of Science:

• Catalysis
• Organic Chemistry
• Computational Chemistry

Background:

• Cooperative catalysis, where multiple catalysts work together, is vital for many organic reactions.
• Discovering new cooperative catalysts is challenging due to combinatorial complexity and reliance on serendipity or prior knowledge.

Purpose of the Study:

• To develop a systematic and cost-effective algorithm for discovering novel cooperative catalytic behaviors.
• To identify synergistic catalyst combinations for challenging organic transformations.

Main Methods:

• A pooling-deconvolution algorithm inspired by group testing was developed to identify cooperative catalyst behaviors.
• The algorithm was validated using simulated data and experimentally on organocatalytic enantioselective oxetane-opening reactions.
• The approach was applied to discover catalysts for palladium-catalyzed decarbonylative cross-coupling.

Main Results:

• The algorithm efficiently identifies cooperative catalyst behaviors with reduced experimental effort.
• Previously unknown cooperativity was validated in organocatalysis.
• Several ligand pairs were identified for palladium-catalyzed decarbonylative cross-coupling, significantly improving efficiency.

Conclusions:

• The pooling-deconvolution algorithm offers a powerful, systematic approach to catalyst discovery.
• This method enables the identification of cooperative catalysts that operate at lower loadings and temperatures.
• The findings open new avenues for designing efficient catalytic systems.