Computational material science datasets have grown at a tremendous pace to fulfill the everincreasing needs of material discovery initiatives. Nearly 3.5 million material structures are available for free in the AFLOW database, 1 155k structures in the Materials Project (MP) database, and 80k in the JARVIS database, to name a few. These databases include material structures along with their properties, which are computed using density functional theory (DFT). This enormous amount of data has driven intense interest in developing machine learning (ML) models to predict DFT quantities with unprecedented accuracies.