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In silico superconductor discovery
Visualizing predictions for a novel material in the Ba-Cu-F-Sr-O family, estimated to have at Tc of 113 K.
Figure 1 from Materials Design for New Superconductors
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].
3DSC - a dataset of superconductors including crystal structures
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.
Machine learning modeling of superconducting critical temperature
Superconductivity has been the focus of enormous research effort since its discovery more than a century ago. Yet, some features of this unique phenomenon remain poorly understood; prime among these is the connection between superconductivity and chemical/structural properties of materials. To bridge the gap, several machine learning schemes are developed herein to model the critical temperatures (Tc) of the 12,000+ known superconductors available via the SuperCon database. Materials are first divided into two classes based on their Tc values, above and below 10 K, and a classification model predicting this label is trained. The model uses coarse-grained features based only on the chemical compositions. It shows strong predictive power, with out-of-sample accuracy of about 92%. https://www.nature.com/articles/s41524-018-0085-8