3DSC - a dataset of superconductors including crystal structures.

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 s

SUBMITTER: Sommer T 

PROVIDER: S-EPMC10663493 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature