A data-intensive approach to mechanistic elucidation applied to chiral anion catalysis

Optimizing a catalyst many ways at once Optimization strategies are often likened to hikes in a hilly landscape. If your goal is to get to the top of the highest hill, and you only take steps toward higher ground, you might never find a peak on a route that requires a preliminary descent. So it is in chemistry, where optimizing each structural feature of a catalyst consecutively might gloss over subtle tradeoffs that in combination offer the best performance. Milo et al. use multidimensional analysis techniques to generate a predictive model of how selectivity depends on multiple characteristics of the catalyst and substrate in a C-N bond-forming reaction (see the Perspective by Lu). They then apply this model to improve the catalyst globally. Science , this issue p. 737 ; see also p. 719