Computational methods for training set selection and error assessment applied to catalyst design: guidelines for deciding which reactions to run first and which to run next
Andrew F. Zahrt, Brennan T. Rose, William T. Darrow, Jeremy Henle, Scott E. Denmark
Abstract
Different subset selection methods are examined to guide catalyst selection in optimization campaigns. Error assessment methods are used to quantitatively inform selection of new catalyst candidates from <italic>in silico</italic> libraries of catalyst structures.
Topics & Concepts
Selection (genetic algorithm)Set (abstract data type)Computer scienceIn silicoCatalysisOperations researchMachine learningEngineeringChemistryProgramming languageBiochemistryGeneMachine Learning in Materials ScienceCatalysis and Hydrodesulfurization StudiesCatalysis and Oxidation Reactions