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Data-driven methods, in particular machine learning, can help to speed up thediscovery of new materials by finding hidden patterns in existing data andusing them to identify promising candidate materials. In the case ofsuperconductors, which are a highly interesting but also a complex class ofmaterials with many relevant applications, the use of data science tools is todate slowed down by a lack of accessible data. In this work, we present a newand publicly available superconductivity dataset ('3DSC'), featuring thecritical temperature $T_\mathrm{c}$ of superconducting materials additionallyto tested non-superconductors. In contrast to existing databases such as theSuperCon database which contains information on the chemical composition, the3DSC is augmented by the approximate three-dimensional crystal structure ofeach material. We perform a statistical analysis and machine learningexperiments to show that access to this structural information improves theprediction of the critical temperature $T_\mathrm{c}$ of materials.Furthermore, we see the 3DSC not as a finished dataset, but we provide ideasand directions for further research to improve the 3DSC in multiple ways. Weare confident that this database will be useful in applying state-of-the-artmachine learning methods to eventually find new superconductors.

3DSC - A New Dataset of Superconductors Including Crystal Structures