We estimate a statistical model to predict the superconducting critical temperature based on the features extracted from the superconductor's chemical formula. The statistical model gives reasonable out-of-sample predictions: ±9.5 K based on root-mean-squared-error. Features extracted based on thermal conductivity, atomic radius, valence, electron affinity, and atomic mass contribute the most to the model's predictive accuracy. It is crucial to note that our model does not predict whether a material is a superconductor or not; it only gives predictions for superconductors.
The study REF also applies random forest to predict the superconducting critical temperature based on the features extracted from the superconductor's chemical formula.
A Data-Driven Statistical Model for Predicting the Critical Temperature of a Superconductor
Computational Materials Science, Volume 154, Pages 346-354 (2018)
Mathematics Materials Science