Given this understanding, we introduced two additional pre-graph encoders to perform learned projections of the column parameters into a distinct embedding space. Global nodes are labelled and their parameters (which are padded to have identical shapes as the node features) are passed to global chromatography feature encoders. We found that separating the integer (viz. one-hot) and float features (1, 2 in Figure ) into separate encoders improved model performance. These encoders are very similar in shape to the node feature encoder, where column features are projected into a fixed-size latent space using two linear layers that project the number of features into an intermediate size of 256, then the embedding dimension (512 for the optimized model), using Rectified Linear Units (ReLUs) activation and dropout layers (p = 0.05). Because this encoder has independent tunable parameters to the node feature encoder, the column parameters can be projected independently of the node features. All the benefits of the node feature encoder apply to the chromatography encoder as well, where a learned representation of column parameters maximizes the flexibility and informativity of the description passed to Graphormer without unnecessarily shoehorning model understanding by over-describing the input.