铁基超导体中$ab$ $initio$有效哈密顿量的数据分析——超导临界温度预测器的构建
Data analysis of $ab$ $initio$ effective Hamiltonians in iron-based superconductors $\unicode{x2014}$ Construction of predictors for superconducting critical temperature
AI总结：
本研究针对强关联材料超导临界温度预测难题，利用数据科学方法分析铁基超导体$ab$ $initio$有效哈密顿量微观参数与$T_\ ext{c}$的关联，构建了线性回归预测模型并提出提温途径，开辟强关联体系材料信息学新方向。
摘要：
高温超导现象出现在铜氧化物、铁基超导体等强关联材料中。学界已开展大量实验与理论工作，以探寻引发高温超导的关键参数。然而，调控高温超导的核心参数仍不明确，这阻碍了对强关联材料超导临界温度（$T_\ ext{c}$）的预测。本研究借助数据科学技术，阐明了铁基超导体中$ab$ $initio$有效哈密顿量的微观参数与实验测得的$T_\ ext{c}$之间的关联。研究通过主成分分析（principal component analysis）证实，微观参数的组合可表征$T_\ ext{c}$随化合物种类变化的规律。研究还构建了一个线性回归模型，能够通过微观参数复现实验测得的$T_\ ext{c}$。基于该回归模型，研究提出了一种通过改变晶格参数提升$T_\ ext{c}$的途径。所开发的方法体系为强关联电子系统的材料信息学开辟了新领域。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
