Data analysis of effective Hamiltonians in iron-based superconductors $\unicode{x2014}$ Construction of predictors for superconducting critical temperature
arXiv:2109.09121 · doi:10.7566/JPSJ.92.064702
Abstract
High-temperature superconductivity occurs in strongly correlated materials such as copper oxides and iron-based superconductors. Numerous experimental and theoretical works have been done to identify the key parameters that induce high-temperature superconductivity. However, the key parameters governing the high-temperature superconductivity remain still unclear, which hamper the prediction of superconducting critical temperatures (s) of strongly correlated materials. Here by using data-science techniques, we clarified how the microscopic parameters in the effective Hamiltonians correlate with the experimental s in iron-based superconductors. We showed that a combination of microscopic parameters can characterize the compound-dependence of using the principal component analysis. We also constructed a linear regression model that reproduces the experimental from the microscopic parameters. Based on the regression model, we showed a way for increasing by changing the lattice parameters. The developed methodology opens a new field of materials informatics for strongly correlated electron systems.
16 pages, 7 figures, 4 tables
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