Machine learning modeling of superconducting critical temperature

Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures ( T c ) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their T c values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%. Separate regression models are developed to predict the values of T c for cuprate, iron-based, and low- T c compounds. These models also demonstrate good performance, with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials. To improve the accuracy and interpretability of these models, new features are incorporated using materials data from the AFLOW Online Repositories. Finally, the classification and regression models are combined into a single-integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database (ICSD) for potential new superconductors. We identify >30 non-cuprate and non-iron-based oxides as candidate materials. Superconductivity: machine learning predicts superconducting transition temperature Machine learning schemes are developed to model the superconducting transition temperature of over 12,000 compounds with good accuracy. A team led by Valentin Stanev from the University of Maryland at College Park and including researchers from Duke University and NIST develops several machine learning schemes to model the critical temperature ( T c ) of over 12,000 known superconductors and candidate materials. They first train a classification model based only on the chemical compositions to categorize the known superconductors according to whether their T c is above or below 10 K. Then they develop regression models to predict the values of T c for various compounds. The accuracy of these models is further improved by including data from the AFLOW Online Repositories. They combine the classification and regression models into a single-integrated pipeline to sear