Abstract

Organic Chemistry Catalysts can introduce asymmetry in the outcome of chemical reactions, favoring one mirror-image product over another. Many of the most effective catalysts for this application were optimized through trial and error, but more recently, parameterization and systematic analysis have played an increasing role. Singh et al. now showcase the predictive power of machine learning applied to the ligands used for asymmetric hydrogenation. A random forest algorithm trained on several different families of chiral binaphthyl phosphorus compounds predicted selectivity in hydrogenation of alkenes and imines with a root-mean-square error of just over 8%. Proc. Natl. Acad. Sci. U.S.A. 117 , 1339 (2020).

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