3DSC - a dataset of superconductors including crystal structures

Timo Sommer; Roland Willa; Jörg Schmalian; Pascal Friederich

doi:10.1038/s41597-023-02721-y

3DSC - a dataset of superconductors including crystal structures

Sci Data. 2023 Nov 21;10(1):816. doi: 10.1038/s41597-023-02721-y.

Authors

Timo Sommer^{1

2

3}, Roland Willa², Jörg Schmalian^{2

4}, Pascal Friederich^{5

6}

Affiliations

¹ Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Engler-Bunte-Ring 8, 76131, Karlsruhe, Germany.
² Institute for Theory of Condensed Matter, Karlsruhe Institute of Technology, Wolfgang-Gaede-Str. 1, 76131, Karlsruhe, Germany.
³ School of Chemistry, Trinity College Dublin, College Green, Dublin, 2, Ireland.
⁴ Institute for Quantum Materials and Technologies, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany.
⁵ Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Engler-Bunte-Ring 8, 76131, Karlsruhe, Germany. pascal.friederich@kit.edu.
⁶ Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany. pascal.friederich@kit.edu.

Abstract

Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, the use of data science tools is to date slowed down by a lack of accessible data. In this work, we present a new and publicly available superconductivity dataset ('3DSC'), featuring the critical temperature T_C of superconducting materials additionally to tested non-superconductors. In contrast to existing databases such as the SuperCon database which contains information on the chemical composition, the 3DSC is augmented by approximate three-dimensional crystal structures. We perform a statistical analysis and machine learning experiments to show that access to this structural information improves the prediction of the critical temperature T_C of materials. Furthermore, we provide ideas and directions for further research to improve the 3DSC. We are confident that this database will be useful in applying state-of-the-art machine learning methods to eventually find new superconductors.

Publication types

Dataset