3DSC - A New Dataset of Superconductors Including Crystal Structures

TL;DR

The paper introduces 3DSC, a comprehensive superconductors dataset that includes crystal structures and critical temperatures, aiming to accelerate discovery through machine learning.

Contribution

It provides a new publicly available dataset with structural data, enhancing predictive models for superconductivity beyond existing databases.

Findings

Structural data improves critical temperature prediction.

Machine learning models benefit from crystal structure information.

The dataset encourages further research in superconductor discovery.

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, 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.