The simple neural network naively embeds the atomic coordinates and labels, exhibiting limited effectiveness. This outcome was anticipated, as the model does not account for the temporal dependency inherent in the trajectory data, where each frame is influenced by its predecessor. However, when employing RNN, LSTM, and GRU models, which inherently treat the data as time-dependent, the results improved significantly. Among these, the RNN outperformed the others, showcasing the highest accuracy in trajectory prediction.