In this work, we use the SuperCon data set for training, similar to what has been done in other implementations , - . Unique, reduced chemical formulae are curated from the NOMAD data set and used for testing. The chemical formulae in the training and testing data are first screened through SMACT for validity and then trained and predicted using the DiSCoVeR algorithm . This results in the screening of novel, chemically valid formulae with predicted critical temperatures.