The GNNs used in the present work follow the message passing neural networks (MPNN) paradigm in which node features are updated from neighboring nodes in a message passing phase, and then the updated features on a node is mapped to a property of interest in a readout phase. The objective of the message passing phase is to learn an embedding for each node, h, such that unique structural fingerprints for the node is encoded. The message passing typically occurs over a certain number of iterations. During iteration t, pairwise interactions between atom i and neighboring atoms j are summed and processed to produce a message