In this study the best performing encoding scheme was like the one implemented in reference 77, where graph node features are generated as learned feature embeddings. The graph feature encoder (1) in figure is replaced with a MLP that projects node features into a fixed-size latent space that is the same size as the Graphormer's embedding dimension. The goal of this encoder is to provide a learned representation of the input features in the graph nodes themselves, such that the model has the maximum flexibility possible for engineering descriptions of local atomic environments while still utilizing human-engineered information. Masters et al. found that the addition or removal of specific features for molecular encoding still provided meaningful changes in model performance. This is likely due to redundant or irrelevant information contained in encodings.