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Augmenting Adaptive Machine Learning with Kinetic Modeling for Reaction Optimization.

We combine random sampling and active machine learning (ML) to optimize the synthesis of isomacroin, executing only 3% of all possible Friedländer reactions. Employing kinetic modeling, we augment machine intuition by extracting mechanistic knowledge and verify that a global optimum was obtained with ML. Our study contributes evidence on the potential of multiscale approaches to expedite the access to chemical matter, further democratizing organic chemistry in a data-motivated fashion.