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

• Published in: Computational materials science Vol. 154; pp. 346 - 354
• Main Author: Hamidieh, Kam
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2018
• Subjects: Critical temperature; Data mining; Machine learning; Statistical learning; Superconductivity; Superconductor; Superconductor; Critical temperature; Data mining; Statistical learning; Superconductivity; Machine learning

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 therm...