Machine learning modeling of superconducting critical temperature

Published

June 28, 2018

Author(s)

Aaron Gilad Kusne, Valentin Stanev, Ichiro Takeuchi

Abstract

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 (Tc) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their Tc 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 Tc 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.

Citation

npj Computational Materials

Volume

4

Issue

1

Pub Type

Journals

Download Paper

https://doi.org/10.1038/s41524-018-0085-8

Local Download

Citation

Kusne, A. , Stanev, V. and Takeuchi, I. (2018), Machine learning modeling of superconducting critical temperature, npj Computational Materials, [online], https://doi.org/10.1038/s41524-018-0085-8, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=924295 (Accessed December 3, 2024)