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Visualizing predictions for a novel material in the Ba-Cu-F-Sr-O family, estimated to have at Tc of 113 K.
Phase diagrams (temperature versus chemical doping or pressure) for four classes of superconductors: hole-doped cuprates like YBa2Cu3O6+x (upper left) [26], κ-(ET)2Cu[N(CN)2]Cl, a 2D-organic (upper right) [27], heavy fermion CeRhIn5 (lower left) [28], and an iron pnictide, Co-doped BaFe2As2 (lower right) [29].
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 Tc of superconducting materials additionally to tested non-superconductors.