As with other diffusion models, TSDiff is subject to their inherent stochasticity, introducing a degree of randomness into the prediction process. Consequently, the TS geometries generated are non-unique, allowing for the generation of multiple conformers. For example, when using TSDiff to sample a single geometry from a withheld test set, 49.1 % of the structures fall within a D-MAE of 0.1 Å from the reference. In contrast, when sampling ten geometries, at least one falls within 0.1 Å of the reference in 84.0 % of cases. This highlights the importance of generating multiple samples to increase the likelihood of identifying the correct conformer. In practice, however, without reference structures it is difficult to determine which of the generated conformers is most accurate.