Data analysis of $ab$ $initio$ effective Hamiltonians in iron-based superconductors $\unicode{x2014}$ Construction of predictors for superconducting critical temperature

TL;DR

This study uses data science to analyze ab initio Hamiltonians in iron-based superconductors, identifying key parameters that influence critical temperatures and proposing a predictive model for T_c based on microscopic parameters.

Contribution

It introduces a novel approach combining principal component analysis and linear regression to predict superconducting T_c from microscopic parameters in strongly correlated materials.

Findings

Principal components characterize compound dependence of T_c.

Linear regression accurately reproduces experimental T_c.

Method suggests ways to enhance T_c by lattice modifications.

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.