Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening
Name
d0re00232a.pdf
Description
Published version
Size
4.03 MB
Format
Adobe PDF
Checksum (MD5)
6bbc16db256a75e04d86d136ff2f8492
Author(s)
Eyke, Natalie S.
Green Jr, William H
Jensen, Klavs F
Date Issued
August 2020
Journal
Reaction Chemistry and Engineering
Publisher
Royal Society of Chemistry (RSC)
Citation
Eyke, Natalie S. et al. “Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening.” Reaction Chemistry and Engineering 5, 10 (August 2020): 1963–1972 © 2020 The Author(s)
Version
Final published version
Abstract
High-throughput reaction screening has emerged as a useful means of rapidly identifying the influence of key reaction variables on reaction outcomes. We show that active machine learning can further this objective by eliminating dependence on "exhaustive"screens (screens in which all possible combinations of the reaction variables of interest are examined). This is achieved through iterative selection of maximally informative experiments from the subset of all possible experiments in the domain. These experiments can be used to train accurate machine learning models that can be used to predict the outcomes of reactions that were not performed, thus reducing the overall experimental burden. To demonstrate our approach, we conduct retrospective analyses of the preexisting results of high-throughput reaction screening experiments. We compare the test set errors of models trained on actively-selected reactions to models trained on reactions selected at random from the same domain. We find that the degree to which models trained on actively-selected data outperform models trained on randomly-selected data depends on the domain being modeled, with it being possible to achieve very low test set errors when the dataset is heavily skewed in favor of low- or zero-yielding reactions. Our results confirm that this algorithm is a useful experiment planning tool that can change the reaction screening paradigm, by allowing medicinal and process chemists to focus their reaction screening efforts on the generation of a small amount of high-quality data.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution Noncommercial 3.0 unported license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1039/D0RE00232A
