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
Abstract:The application of machine learning (ML) to problems in homogeneous catalysis has
emerged as a promising avenue for catalyst optimization. An important aspect of such optimization
campaigns is determining which reactions to run at the outset of experimentation and which future
predictions are the most reliable. Herein, we explore methods for these two tasks in the context of
our previously developed chemoinformatics workflow. First, different methods for training set
selection are compared, including algorithm… Show more
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