Augmenting Adaptive Machine Learning with Kinetic Modeling for Reaction Optimization.

Area of Science:

• Organic Chemistry
• Computational Chemistry
• Chemical Synthesis

Background:

• Optimizing organic synthesis is crucial for accessing novel chemical compounds.
• Traditional synthesis optimization can be time-consuming and resource-intensive.

Purpose of the Study:

• To develop and apply a data-driven, multiscale approach for optimizing the synthesis of isomacroin.
• To demonstrate the efficiency of combining random sampling with active machine learning (ML).

Main Methods:

• Utilized active machine learning (ML) combined with random sampling.
• Performed only 3% of all possible Friedländer reactions for isomacroin synthesis.
• Integrated kinetic modeling to extract mechanistic insights and augment ML predictions.

Main Results:

• Achieved significant optimization in isomacroin synthesis.
• Verified the global optimum using ML, reducing experimental workload.
• Demonstrated the power of multiscale approaches in chemical synthesis.

Conclusions:

• The synergistic combination of ML and kinetic modeling accelerates chemical discovery.
• This data-motivated strategy democratizes organic chemistry by improving efficiency.
• Multiscale approaches offer a powerful paradigm for expediting access to diverse chemical matter.