My hypothesis was that an equivariant neural network would geometrically transform the molecule in a consistent manner, preserving its structural integrity. The E3NN's performance was comparable to the RNN, achieving a trajectory prediction markedly better than the baseline. Using spherical harmonics also allowed the E3NN to account for how atomic interactions change with the orientation of the molecule, which is particularly important for dynamic systems like molecular trajectories where relative positioning and orientation play a significant role. Some challenges the E3NN may face include fully capturing all the nuanced movements, features, and interactions within the molecule over time and overcoming the computational intensity required for processing E3NNs. Nonetheless, balancing the model's complexity with computational feasibility is key for aspirin and moreso for more complex molecules and trajectories.