Previous work with GCNN surrogate models in materials science includes crystal graph convolutional neural networks (CGCNN) and material graph network (MEGNet) . The focus of these efforts was on using GCNNs for prediction of material properties across broad classes of stoichiometric, intermetallic, and ordered crystalline materials, sourced from the Materials Project and the Open Quantum Materials Database (OQMD) . This work showed significant flexibility of GCNNs, simultaneously handling many materials across different properties, with good accuracy relative to the original density functional theory (DFT) results. More recent work started investigating the effectiveness of GCNN models for prediction of mixing enthalpy, atomic charge transfer, and atomic magnetic moment for solid solution ferromagnetic alloys, characterized by disordered atomic configurations, with non-relaxed crystal structure and fixed volume from open-source DFT data . Results showed that the GCNN model can accurately estimate material properties as a function of the configurational entropy .