TFNs are specialised NNs designed to use rotationally equivariant, continuous convolutions to process 3D point clouds, such as molecular geometries, and output scalar, vector and higher-order tensor predictions. TSNet modified TFNs in a number of ways, including a Siamese architecture that allows the model to accept any number of input molecules. For TS geometry prediction, this enables TSNet to take in the Cartesian coordinates of both the reactant and product structures and output a TS geometry directly. TSNet was trained on an author created SN2 reaction dataset containing only 53 reactions, achieving a validation D-MAE of 0.18 Å. Further improvements were achieved by applying a confidence threshold, arbitrarily set at 0.5, where only the TS structures generated with a confidence above this threshold were accepted ("OA-ReactDiff+rec. (p > 0.5)"). By excluding any reactions where the TSs generated did not meet the threshold (14 % of the test set), test mean and median RMSDs for TS geometry prediction decreased to 0.106 Å and 0.047 Å, respectively, while test mean and median activation barrier errors decreased to 3.1 kcal mol -1 and 1.4 kcal mol -1 , respectively. The best performing workflow resulted from the integration of "OAReactDiff+rec." with traditional QM methods, using DFT-based NEB optimisations to find the TS for any system where all generated TS structures fell below the confidence threshold. This resulted in a test activation barrier MAE of 2.6 kcal mol -1 . A summary of the metrics reported by model can be found in Table . Two major limitations of OA-ReactDiff were discussed by the authors. First, the methodology has a scaling limitation for chemical systems greater than 100 atoms, as the message-passing update requires building a fully-connected graph of the reactant, TS and product geometries, leading to three times the computational cost. The second is the stochastic nature of the diffusion model, which increases the total runtime due to the generation of 40 structures for each system and introduces uncertainty in the sample quality of the generated TS structure. Furthermore, this solution still relies upon costly QM optimisation to achieve accurate predictions for the whole test set. However, the integration of "OA-ReactDiff+rec. (p > 0.5)" with DFT-based NEB provides an end-to-end workflow that is a good compromise between computational efficiency and accuracy.