In chemical graph neural networks (GNNs), crystalline structures can be represented as graphs in which each atom is represented by a node, v, and relationships between nodes are represented by an edge, e, which are commonly thought of as chemical bonds, coulombic interactions, etc. Often the notion of a chemical bond is ill-defined in a crystalline structure, so edges are frequently constructed as all pair-wise node-connections within some cutoff radius, r cut , about each atom, taking into account the periodic boundary conditions of the system. All together, the set of nodes, V = {v 1 , v 2 , ..., v N }, and the set of edges, E = {e 1 , e 2 , ..., e M }, make up a graph, G(V, E). To make the graph G amenable to machine learning, each node is assigned a feature vector (information of the atomic number and the Cartesian coordinates of the atom in this work). The node data may also be processed to create edge features, e ij , which encode positional information between the two nodes, i and j.