GCNNs are DL models for processing graph data. Representing crystal structures in the form of graphs is natural since the atoms can be viewed as nodes and metallic bonds as edges of the graph. Nodes in the graph retain atomic features in crystal structures and edges in the graph retain the connectivity and (optionally) the mutual distance between nodes. Each graph associated with a specific crystallographic structure can have global graph-level properties such as the formation energy and the bulk modulus.