The Development of Multidimensional Analysis Tools for Asymmetric Catalysis and Beyond

Area of Science:

• Organic Chemistry
• Catalysis
• Physical Organic Chemistry

Background:

• Modern organic chemistry reports often omit suboptimal reaction results, hindering comprehensive understanding.
• Asymmetric catalysis optimization typically focuses on yield and selectivity, overlooking crucial outlier data.
• Unexplained performance variations in reactions and catalyst evaluations are frequently disregarded.

Purpose of the Study:

• To develop a strategy that integrates reaction optimization with mechanistic interrogation.
• To leverage all experimental data, including outliers, for deeper chemical insights.
• To advance catalyst development and de novo reaction design by analyzing previously ignored data.

Main Methods:

• Correlating reaction outputs (selectivity, potential) with structural descriptors of molecules.
• Integrating techniques from synthetic methodology, mechanistic studies, statistics, computational chemistry, and data science.
• Analyzing data sets with simultaneous changes in reagent, substrate, and catalyst structures.

Main Results:

• Demonstrated a method to extract valuable mechanistic information from reaction outliers.
• Developed tools that integrate diverse data types for a holistic reaction analysis.
• Showcased the application of this strategy across various case studies with complex structural variations.

Conclusions:

• Wasting no data in reaction optimization provides a powerful entry point for new discoveries.
• This integrated approach represents a modern paradigm in physical organic chemistry.
• The strategy facilitates the development and probing of mechanistic hypotheses for complex chemical processes.