Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening
Natalie S. Eyke, William H. Green, Klavs F. Jensen
Abstract
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.
Topics & Concepts
Active learning (machine learning)Computer scienceMachine learningSelection (genetic algorithm)Artificial intelligenceIterative learning controlDomain (mathematical analysis)MathematicsControl (management)Mathematical analysisMachine Learning in Materials ScienceComputational Drug Discovery MethodsInnovative Microfluidic and Catalytic Techniques Innovation