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