Predicting the Critical Temperature of a Superconductor

Superconductivity is a phenomenon of exactly zero electrical resistance and expulsion of magnetic flux fields occurring in certain materials, called superconductors, when cooled below a characteristic critical temperature. One of the applications is in the Magnetic Resonance Imaging (MRI) systems which is widely performed by health case professionals for detailed internal body imaging. Predicting the critical temperature (Tc) of a superconductor is still an open problem in the scientific community. In the past, simple empirical rules based on experiments have guided researchers in synthesizing superconducting materials for many years. Nowadays, features (or predictors) based on the superconductor’s elemental properties can be generated and used to predict Tc. In this task, we are going to analyze superconductor data from the Superconducting Material Database maintained by Japan’s National Institute for Materials Science (NIMS). The aim is to build statistical models that can predict Tc based on the material’s chemical properties.