A data-driven statistical model for predicting the critical temperature of a superconductor

By K. Hamidieh. Computational materials science, 2018

307 citations recorded by the source corpus.

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: $\pm 9.5$ 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.

Subfields: Atomic and Molecular Physics, and Optics, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/b3bea0ac481f0869cb746f3b44d5689bf1a9b924