  The advent of computational material sciences has paved the way for
data-driven approaches for modeling and fabrication of materials. The
prediction of properties like the glass-forming ability (GFA) by using the
variation in alloy composition remains to be a challenging problem in the field
of material sciences. It also results in significant financial concerns for the
manufacturing industry. Despite the existence of various empirical guides for
the prediction of GFA, a comprehensive prediction model is still highly
desirable. This work focuses on studying some of the popular machine learning
algorithms for the prediction of the reduced glass transition temperature (Trg)
of material compositions. From the experimentation, we conclude that the
ensemble model performs better for predicting Trg. This result can prove
instrumental in the branch of material sciences by helping us to develop
materials having remarkable properties.
