Materials informatics has shown that the cuprate class of superconductors contains a highly unexplored materials space that has yet to be explored . This is why formulae from the SuperCon database are used for the training of our model. Of these formulae, "roughly 5,700 compounds are cuprates and 1,500 are iron-based (about 35 and 9 percent, respectively), reflecting the significant research efforts invested in these two families. The remaining set of about 8,000 is a mix of various materials, including conventional phonon-driven superconductors (e.g., elemental superconduc-Fig. : Workflow of implementation. 12,415 formulae from SuperCon and 694,398 formulae from NOMAD are first verified through SMACT. After featurization and training of the SuperCon data through DiSCoVeR, chunks of the NOMAD data are screened through the model tors, A15 compounds), known unconventional superconductors like the layered nitrides and heavy fermions, and many materials for which the mechanism of superconductivity is still under debate (such as bismuthates and borocarbides)" . The compositions in SuperCon were reduced using the get_reduced_composition_and_factor() method from the pymatgen.core.Composition class. After some data cleaning, the SuperCon training data was reduced to 12,415 formulae of superconductors and their critical temperatures. Fig. shows the distribution of the SuperCon data set after cleaning.