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

We explore the possibility to control the superconducting (SC) transition temperature at optimal hole doping T_c^ opt in cuprates by tuning the chemical formula (CF). T_c^ opt can be theoretically predicted from the parameters of the ab initio low-energy effective Hamiltonian (LEH) with one antibonding (AB) Cu3d_x^2-y^2/O2p_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 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_ X and R_ A of the apical anion X and cation A in the block layer. (|t_1| increases when R_ X or R_ A decreases.) (b) u has a main-order dependence on the negative ionic charge Z_ X of X and the hole doping δ of the AB orbital. (u decreases when |Z_ 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.