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Universal Chemical Formula Dependence of $Ab$ $Initio$ Low-Energy Effective Hamiltonian in Single-Layer Carrier Doped Cuprate Superconductors -- Study by Hierarchical Dependence Extraction Algorithm (2403.07525v1)

Published 12 Mar 2024 in cond-mat.supr-con and cond-mat.str-el

Abstract: 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$3d_{x2-y2}$/O$2p_{\sigma}$ 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 $\delta$ of the AB orbital. ($u$ decreases when $|Z_{\rm X}|$ increases or $\delta$ 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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