Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening
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 possi...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Royal Society of Chemistry (RSC)
2021
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| Online Access: | https://hdl.handle.net/1721.1/129381 |