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Materials Informatics Reveals Unexplored Structure Space in Cuprate Superconductors
Author(s) -
Goodall Rhys E. A.,
Zhu Bonan,
MacManusDriscoll Judith L.,
Lee Alpha A.
Publication year - 2021
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.202104696
Subject(s) - cuprate , materials science , superconductivity , transformative learning , space (punctuation) , data set , computer science , data mining , condensed matter physics , doping , physics , optoelectronics , artificial intelligence , psychology , pedagogy , operating system
High‐temperature superconducting cuprates have the potential to be transformative in a wide range of energy applications. In this work, the corpus of historical data about cuprates is analyzed using materials informatics, re‐examining how their structures are related to their critical temperatures (Tc). The available data is highly clustered and no single database contains all the features of interest to properly examine trends. To work around these issues a linear calibration approach that allows the utilization of multiple data sources is employed, combining fine resolution data for which the Tc is unknown with coarse resolution data where it is known. The hybrid data set constructed enables the exploration of the trends in Tc with the apical and in‐plane copper–oxygen distances. It is shown that large regions of the materials space have yet to be explored. Novel experiments relying on the nano‐engineering of the crystal structure may enable the exploration of such new regions. Based on the trends identified it is proposed that single layer Bi‐based cuprates are good candidate systems for such experiments.