There are two primary methods for obtaining this energy barrier: (i) performing a single-point QM calculation to determine the energy of the predicted TS geometry and comparing it to the energy of the corresponding optimised reactants, or (ii) using the geometry as input to an ML model trained to predict the activation energy. Few models in the literature adopt or report these approaches. Additionally, achieving an accuracy within 1.0 kcal mol -1 would serve as a valuable target for TS geometry prediction models. Spiekermann et al. has already examined the effects of using perturbed structures to predict energy barriers by inputting noised TS geometries into DimeNet++, as was demonstrated in Table . It is also important to note that small perturbations in molecular geometries can substantially impact their energy as calculated by QM methods. To illustrate this, we optimised a TS for the SN2 reaction between methylamine and 1-chloropropane using B3LYP/6-31G(d) in Gaussian16. Random positional adjustments were introduced to generate 20 structures, each with an RMSD of 0.100 Å from the optimised geometry. Single-point energy calculations were then performed on the 20 perturbed structures. The MAE in energy between the optimised and perturbed geometries was 57.4 kcal mol -1