Superconductivity is a fascinating quantum mechanical phenomenon where a material, under certain conditions, can exhibit zero electrical resistance, allowing electrical current to flow without loss of energy. The temperature at which a material transitions into a superconducting state is termed its "Critical Temperature". This parameter is crucial for applications involving superconductors, including energy storage, transportation, and advanced computing systems. Accurate prediction of the critical temperature for a given superconducting material can offer invaluable insights into material design, facilitate optimization processes, and accelerate technological innovations. However, predicting Critical Temperature has remained a significant challenge, given the complex interplay of variables such as material composition, structural features, and external conditions.
The objective of this case study is to develop a robust predictive model for the critical temperature (Tc) of superconducting materials. Specifically, we aim to construct a Linear Regression model optimized with L1 or L2 regularization techniques—or a combination of both, known as Elastic Net regularization—to predict Tc as accurately as possible. Regularization methods like L1 (Lasso) and L2 (Ridge) can prevent overfitting, thereby making the model generalizable to unseen data. Additionally, these techniques can assist in feature selection, an essential aspect when dealing with high-dimensional data sets commonly encountered in materials science.