Machine learnt patterns in rhodium-catalysed asymmetric Michael addition using chiral diene ligands
Interpretable featurisation allows Quantitative Structure-Property Relationships (QSPR) between chiral diene ligand structures and product stereoselectivities in rhodium-catalysed asymmetric Michael additions (RhCASA). The machine learning approach developed herein is simple to implement yet provide...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-01-01
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| Series: | Results in Chemistry |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211715622000984 |