Learned Features

Learned features are extracted automatically by the network during training.

All static features are transformed through several neural network layers to produce learned features. This results in learned features (based on static features) that are more expressive and better suited for the specific task. The features are understoof per atom-pair and are later aggregated to form a complete representation per node = atom.