Data analysis of ab initio effective Hamiltonians in iron-based superconductors x2014 Construction of predictors for superconducting critical temperature
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 (Tcs) of strongly correlated materials. Here by using data-science techniques, we clarified how the microscopic parameters in the ab initio effective Hamiltonians correlate with the experimental Tcs in iron-based superconductors. We showed that a combination of microscopic parameters can characterize the compound-dependence of Tc using the principal component analysis. We also constructed a linear regression model that reproduces the experimental Tc from the microscopic parameters. Based on the regression model, we showed a way for increasing Tc by changing the lattice parameters. The developed methodology opens a new field of materials informatics for strongly correlated electron systems.