Database curation and assessment for the prediction of superconductors

Superconducting materials exhibit zero electrical resistance and perfect diamagnetism below a certain critical temperature. They have the potential to revolutionize many industries, including power transmission, transportation, and computing. However, the discovery of new superconducting materials is a slow and expensive process. Moreover, carefully curated databases for superconducting materials do not exist yet.

We present a curated database of superconductors that can be used to accelerate the discovery of new materials. The database is based on data from the Crystallographic Open Database (COD) and the Handbook of Superconductivity. We have filtered the data for duplicates and structural predictions. We also label compounds with their crystallographic space groups and lattice parameters. Subsequently, each compound is labeled according to its superconducting state, where the information is available.

Our database is a valuable resource for researchers who are interested in predicting new superconductors. It can be used to train machine learning models to identify promising candidates for further study. The database is also useful for understanding the relationship between the structural and electronic properties of superconductors.