Differing in the policy adopted to aggregate, transfer, and update nodal information through the message passing policy, a variety of GCNNs have been developed, namely crystal GCNNs (CGCNN) , and principal neighborhood aggregation (PNA) . Their corresponding graph convolutional layers are provided by the open-source PyTorch Geometric library , namely CGConv and PNAConv, and can be used by our implementation of GCNN models called HydraGNN . CGConv and PNAConv differ in the policy used to update node features using information coming from adjacent nodes in the graph. CGConv was specifically developed for atomic crystals, and uses the sum operator to aggregate information from adjacent nodes, whose importance is tuned via a learnable scaling factor. PNAConv combines multiple aggregation policies (e.g., mean, maximum, minimum, standard deviation) to enhance the discriminative power of the model in distinguishing different messages when using a graph convolutional layer, thus resulting in a better preservation of graph isomorphism. Similarly to CGConv, PNAConv also relies on learnable scaling operators to assign different importance to different adjacent nodes.