In recent years due to the availability of large experimental and computational datasets, improved algorithms, and the growth of computer processing power, machine learning has emerged as an efficient tool to narrow down relevant composition domains, accelerate new material discoveries, and to design efficient experimental methods based on optimization algorithms . In fact, accurate predictions of material properties have been shown using composition alone! For example, Goodall et al. have developed a machine learning approach referred as Roost (Representation Learning from Stoichiometry), which takes only the material composition as input in the form of a dense weighted graph and successfully predicts material properties . Similarly, CrabNet (Compositionally restricted attention-based network), a machine learning network based on the Transformer attention mechanism, takes material descriptors as input and predicts the material properties. CrabNet performs exceptionally well in almost all 28 benchmark datasets . Not only in predicting material properties, but machine learning has also successfully applied to new material discoveries . Tehrani et al. discovered two promising super-hard materials, rhenium tungsten carbide and molybdenum tungsten borocarbide, by training a support vector regressor model on computational DFT data followed by screening from an extensive known crystal structure database . The reported hardness value for each of these newly discovered compounds exceeds 40 GPa at low load (0.49 N).