The decision to use a GCN was grounded in its ability to effectively represent and process graph-structured data, preserve bond connectivities, and capture local interactions. By training the molecule as a graph, the model naturally conserves the bond relationships between atoms, which is crucial for accurately predcting molecular dynamics. Furthermore, the convolutional layers of the GCN allow equivariance for rototranslation and node permutation. Interesting, the GCN's method of merely embedding atomic labels and coordinates outperformed all previous models tried in the study, suggesting that even a relatively straightforward graph-based approach can be quite powerful in capturing the essential dynamics of molecular structures. While GCNs generally offered more computationally efficiency than E3NN models, it focuses on local interactions and may not fully capture long-range effects or more complex dynamics without additional layers and does not guarantee similar results for more complex molecules.