Designing efficient catalysts is one of the ultimate goals of chemists. In this
Perspective, we discuss how local electric fields (LEFs) can be exploited to improve the
catalytic performance of supramolecular catalysts, such as enzymes. More specifically,
this Perspective starts by laying out the fundamentals of how local electric fields
affect chemical reactivity and review the computational tools available to study
electric fields in various settings. Subsequently, the advances made so far in
optimizing enzymatic electric fields through targeted mutations are discussed critically
and concisely. The Perspective ends with an outlook on some anticipated evolutions of
the field in the near future. Among others, we offer some pointers on how the recent
data science/machine learning revolution, engulfing all science disciplines, could
potentially provide robust and principled tools to facilitate rapid inference of
electric field effects, as well as the translation between optimal electrostatic
environments and corresponding chemical modifications.