Essential to Equation 1 is that it covers a broad range of reaction rates, from those that are virtually undetectable (k 20 • C < 10 -5 M -1 s -1 ) to those that are diffusion-controlled (k 20 • C > 10 9 M -1 s -1 ). However, experimentally measuring these reactivity parameters can be quite labor-intensive as well as difficult for reactions at the extreme ends of the reactivity scale. To streamline this process, various computational approaches have been developed. This includes estimating the rate constant using the Eyring equation and computing the reactivity parameters from frontier molecular orbital (FMO) energies or chemical affinities. 12-14 Moreover, several machine learning (ML) approaches have recently emerged based on Mayr's database which currently holds experimental reactivity parameters for 355 electrophiles and 1,300 nucleophiles. Our recent work introduces ESNUEL, 22 a fully automated quantum chemistry (QM)based workflow for EStimating NUcleophilicity and ELectrophilicity. A workflow that builds upon studies by Van Vranken and Baldi showing that calculated methyl cation affinities (MCAs) and methyl anion affinities (MAAs) of structurally different molecules correlate with Mayr's N • s N and E, respectively, when accounting for solvent effects. While this approach provides excellent generalizability with a median wall time of less than two minutes per molecule using eight CPU cores for 2,341 molecules (averaging ∼ 10 heavy atoms and ∼ 6 identified electrophilic and nucleophilic sites), further reducing the wall time to seconds or sub-seconds would be advantageous for many applications such as computer-aided synthesis planning (CASP).