Revealing Kinetic Features of a Macrocyclization Reaction Using Machine-Learning-Augmented Data.

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Chemical Kinetics

Background:

• Macrocyclization is crucial in synthesizing complex molecules.
• Understanding reaction kinetics is key to optimizing synthetic routes.
• Predictive models often lack mechanistic interpretability.

Purpose of the Study:

• To develop a method for elucidating macrocyclization kinetics using machine learning.
• To bridge the gap between predictive modeling and mechanistic understanding.
• To provide quantitative kinetic parameters for macrocyclization reactions.

Main Methods:

• Augmenting experimental yield data using machine learning.
• Performing kinetic analyses on concentration-dependent yields.
• Applying least-squares fitting to differential rate equations.
• Calculating rate constants and effective molarity.

Main Results:

• Expanded 36 experimental datasets to 200 using machine learning.
• Elucidated the underlying chemistry of predictive models.
• Obtained quantitative kinetic parameters (rate constants, effective molarity).
• Identified linear relationships between effective molarity and strain energy.

Conclusions:

• Machine learning-augmented data enables detailed kinetic analysis of macrocyclization.
• Quantitative insights provide physicochemical explanations for optimal reaction conditions.
• Effective molarity and strain energy are key factors in oligomeric macrocyclization.