The discovery of new materials is needed for a wide range of applications from batteries, to catalysts, to solar cells, and more. Any new material is only of practical interest if it is stable, and therefore, the formation energy is an important property to predict for any new proposed material. Density functional theory (DFT) has long been used to study a wide variety of material properties, including formation energy. High throughput DFT calculations have been used to screen for stability and in some cases have yielded large databases of crystal structures and their formation energies. These databases have been used by many researchers for training machine learning (ML) systems to predict the stability of materials and attempt to discover new, stable materials.