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Through iterative selection of maximally informative experiments, active learning renders exhaustive screening obsolete. Chosen experiments are used to train models that are accurate over the entire domain, thus reducing the experiment burden.

Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening