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Deriving intuition in catalyst design with machine learning
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DOI：10.1016/j.chempr.2021.12.006.png)
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Catalysts for asymmetric syntheses provide a powerful means to manipulate and obtain matter with defined stereochemistry. In a recent issue of Cell Reports Physical Science, Kanai and colleagues report on a data-driven approach to identify key components in iridium/boron catalysts for the on-demand synthesis of stereochemically defined alpha-allyl carboxylic acids.
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