A more accurate approach to generate the descriptor vector could involve concatenating the CM5 charges for each protonated site within the molecule or by taking the difference between the neutral descriptor vector and the protonated descriptor vector for each site, thereby providing a better representation of the molecule. It is most likely that the training set (80 %; 595 compounds; 2607 hydricities) contains no to few compounds or types of sites that we see in the test set. Thus, the ML model has difficulty distinguishing the different sites where, e.g., the difference between an EWG or EDG in the vicinity significantly impacts the carbocation stability. Therefore, a more extensive training set for our ML model would be advantageous.