The rationale behind using diffusion models centered on their generative capabilities, which are particularly suited for tasks where the output is not a direct mapping from the input. In the context of molecular trajectories, this means generating plausible molecular conformations at each time step, based on the learned distribution of atomic positions and movements. The strength of this model lies in its ability to generate new data points that are not explicitly present in the training set, which can be particularly useful for predicting future states. The biggest challenge is maintaining physical plausibility and equivariance. The model may be able to capture broad patterns and generate new data, but it is difficult to detail intricate molecular motions.