3DSC - A New Dataset of Superconductors Including Crystal Structures
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, which are a highly interesting but also a complex class of
materials with many relevant applications, 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 $T_\mathrm{c}$ 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 the approximate three-dimensional crystal structure of
each material. We perform a statistical analysis and machine learning
experiments to show that access to this structural information improves the
prediction of the critical temperature $T_\mathrm{c}$ of materials.
Furthermore, we see the 3DSC not as a finished dataset, but we provide ideas
and directions for further research to improve the 3DSC in multiple ways. We
are confident that this database will be useful in applying state-of-the-art
machine learning methods to eventually find new superconductors.