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arXiv 2403.07525cond-mat.supr-concond-mat.str-el
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单层载流子掺杂铜酸盐超导体中Ab Initio低能有效哈密顿量的普适化学式依赖性——基于分层依赖提取算法的研究

Universal Chemical Formula Dependence of $Ab$ $Initio$ Low-Energy Effective Hamiltonian in Single-Layer Carrier Doped Cuprate Superconductors -- Study by Hierarchical Dependence Extraction Algorithm

Jean-Baptiste Morée, Ryotaro Arita

AI总结:

本文提出单层空穴掺杂铜酸盐超导体中低能有效哈密顿量参数对化学式和结构特征的普适依赖关系,利用分层依赖提取(HDE)机器学习算法揭示了跳跃幅度和库仑排斥比的关键物理依赖,为优化超导特性提供基础。

摘要:

我们探索了通过调节化学式(CF)来控制铜酸盐中最优空穴掺杂下的超导(SC)转变温度$T_{c}^{\ m opt}$的可能性。$T_{c}^{\ m opt}$可以通过\ extit{ab initio}低能有效哈密顿量(LEH)的参数进行理论预测,该哈密顿量在CuO$_2$平面内每个Cu原子包含一个反键(AB)Cu$3d_{x^2-y^2}$/O$2p_σ$轨道,主要参数为最近邻跳跃幅度$|t_1|$和比值$u=U/|t_1|$,其中$U$为在位有效库仑排斥能。然而,$|t_1|$和$u$对CF的依赖关系是一个高度复杂的问题。本文提出了在夹于阻挡层之间的单层CuO$_2$空穴掺杂铜酸盐中,$|t_1|$和$u$对CF及结构特征的普适依赖关系。为此,我们对$|t_1|$和$u$进行了广泛的\ extit{ab initio}计算,并采用名为分层依赖提取(HDE)的机器学习方法分析了结果。主要结果如下:(a) $|t_1|$对阻挡层中顶端阴离子X和阳离子A的半径$R_{\ m X}$和$R_{\ m A}$具有主阶依赖性。(当$R_{\ m X}$或$R_{\ m A}$减小时,$|t_1|$增大。)(b) $u$对X的负离子电荷$Z_{\ m X}$和AB轨道的空穴掺杂$δ$具有主阶依赖性。(当$|Z_{\ m X}|$或$δ$增大时,$u$减小。)我们阐明并讨论了和的微观机制。通过展示和与以往研究结果的一致性,我们证明了HDE的预测能力。本研究结果为优化铜酸盐及可能类似材料的超导特性提供了基础,同时HDE方法为识别物理量之间的依赖关系提供了一个通用平台。

英文摘要:

We explore the possibility to control the superconducting (SC) transition temperature at optimal hole doping $T_{c}^{\rm opt}$ in cuprates by tuning the chemical formula (CF). $T_{c}^{\rm opt}$ can be theoretically predicted from the parameters of the \textit{ab initio} low-energy effective Hamiltonian (LEH) with one antibonding (AB) Cu$3d_{x^2-y^2}$/O$2p_σ$ orbital per Cu atom in the CuO$_2$ plane, notably the nearest neighbor hopping amplitude $|t_1|$ and the ratio $u=U/|t_1|$, where $U$ is the onsite effective Coulomb repulsion. However, the CF dependence of $|t_1|$ and $u$ is a highly nontrivial question. In this paper, we propose the universal dependence of $|t_1|$ and $u$ on the CF and structural features in hole doped cuprates with a single CuO$_2$ layer sandwiched between block layers. To do so, we perform extensive \textit{ab initio} calculations of $|t_1|$ and $u$ and analyze the results by employing a machine learning method called Hierarchical Dependence Extraction (HDE). The main results are the following: (a) $|t_1|$ has a main-order dependence on the radii $R_{\rm X}$ and $R_{\rm A}$ of the apical anion X and cation A in the block layer. ($|t_1|$ increases when $R_{\rm X}$ or $R_{\rm A}$ decreases.) (b) $u$ has a main-order dependence on the negative ionic charge $Z_{\rm X}$ of X and the hole doping $δ$ of the AB orbital. ($u$ decreases when $|Z_{\rm X}|$ increases or $δ$ increases.) We elucidate and discuss the microscopic mechanism of (a,b). We demonstrate the predictive power of the HDE by showing the consistency between (a,b) and results from previous works. The present results provide a basis for optimizing SC properties in cuprates and possibly akin materials. Also, the HDE method offers a general platform to identify dependencies between physical quantities.

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