Data Analysis of Ab initio Effective Hamiltonians in Iron-Based Superconductors — 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 ($T_\text{c}$s) 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 $T_\text{c}$s in iron-based superconductors. We showed that a combination of microscopic parameters can characterize the compound-dependence of $T_\text{c}$ using the principal component analysis. We also constructed a linear regression model that reproduces the experimental $T_\text{c}$ from the microscopic parameters. Based on the regression model, we showed a way for increasing $T_\text{c}$ by changing the lattice parameters. The developed methodology opens a new field of materials informatics for strongly correlated electron systems.