Deriving intuition in catalyst design with machine learning

Abstract

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 α-allyl carboxylic acids. 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 α-allyl carboxylic acids. Data-driven catalyst optimization for stereodivergent asymmetric synthesis by iridium/boron hybrid catalysisChen et al.Cell Reports Physical ScienceDecember 6, 2021In BriefChen et al. report that data-driven catalyst design facilitates stereodivergent asymmetric synthesis, which remains challenging in organic synthesis. This research indicates that digital transformations (DXs) of organic synthesis (e.g., the use of machine-leaning and materials-informatics techniques) enable analysis and control of complicated reactions, opening new avenues in molecular catalysis. Full-Text PDF Open Access