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We estimate a statistical model to predict the superconducting criticaltemperature based on the features extracted from the superconductor's chemicalformula. The statistical model gives reasonable out-of-sample predictions: $\pm9.5$ K based on root-mean-squared-error. Features extracted based on thermalconductivity, atomic radius, valence, electron affinity, and atomic masscontribute the most to the model's predictive accuracy. It is crucial to notethat our model does not predict whether a material is a superconductor or not,it only gives predictions for superconductors.

A Data-Driven Statistical Model for Predicting the Critical Temperature of a Superconductor