Diffusion models are a class of generative models that operate through a two-step forward and reverse diffusion process. The former progressively noises a sample until it approximates a normal distribution, and the latter reconstructs the original data distribution from the Gaussian noise. These models have been applied across many areas, including image synthesis, computer vision and temporal data modelling, as well as in chemistry for organic molecule generation, protein ligand docking and de novo drug design. One aspect of diffusion models which makes them a promising approach for TS prediction is the iterative refinement process, which enables the model to capture complex spatial information effectively -a capability demonstrated in various computer vision tasks. Additionally, they exhibit stable training dynamics and offer some interpretability, as the incremental transformation from noise to the final output can be visualised. However, diffusion models typically require large amounts of training data, which can be costly to obtain. Furthermore, during inference a single TS requires numerous iterative denoising steps, making the prediction of the TS geometry considerably more expensive than other ML models which require only a single model evaluation (see below). Despite this, it is important to note that the computational expense of the diffusion process at inference remains significantly lower than that of QM methods, such as DFT. For more information on diffusion models, please refer to Refs. .