A Data Driven Statistical Model to Predict Critical Temperature of Superconducting Material

We estimate a statistical model to predict the critical temperature of superconducting materials based on the features extracted from the constituent elements. The statistical model gives reasonable out of sample predictions: +/- 10 on average based on root-mean-squared-error. The model does not provide a simple picture of the relationships between the features and critical temperature. However, we are able to examine the feature importance in prediction accuracy. It is also crucial to note that our model does not predict whether a material is a superconductor or not; it only gives predictions for superconducting materials.