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We explore the possibility to control the superconducting (SC) transition temperature at optimal hole doping Tcᵒᵖᵗ in cuprates by tuning the chemical formula (CF). Tcᵒᵖᵗ can be theoretically predicted from the parameters of the ab initio low-energy effective Hamiltonian (LEH) with one antibonding (AB) Cu3dx²-y²/O2pσ orbital per Cu atom in the CuO₂ plane, notably the nearest neighbor hopping amplitude |t₁| and the ratio u=U/|t₁|, where U is the onsite effective Coulomb repulsion. However, the CF dependence of |t₁| and u is a highly nontrivial question. In this paper, we propose the universal dependence of |t₁| and u on the CF and structural features in hole doped cuprates with a single CuO₂ layer sandwiched between block layers. To do so, we perform extensive ab initio calculations of |t₁| and u and analyze the results by employing a machine learning method called Hierarchical Dependence Extraction (HDE). The main results are the following: (a) |t₁| has a main-order dependence on the radii RX and RA of the apical anion X and cation A in the block layer. (|t₁| increases when RX or RA decreases.) (b) u has a main-order dependence on the negative ionic charge ZX of X and the hole doping δ of the AB orbital. (u decreases when |ZX| 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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Moree et al. (2024) studied this question.
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