Augmenting Adaptive Machine Learning with Kinetic Modeling for Reaction Optimization
A. Filipa Almeida, Filipe Ataíde, Rui M. S. Loureiro, Rui Moreira, Tiago Rodrigues
Abstract
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.
Topics & Concepts
ChemistryIntuitionAdaptive samplingMachine learningArtificial intelligenceBiochemical engineeringBiological systemComputer scienceCognitive scienceMathematicsBiologyPsychologyEngineeringMonte Carlo methodStatisticsMachine Learning in Materials ScienceComputational Drug Discovery MethodsInnovative Microfluidic and Catalytic Techniques Innovation