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Description
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This dataset contains the input files, output data, and post-processing scripts associated with the DFT+DMFT study of the copper-substituted lead–vanadium bromine apatite Pb9Cu(VO4)6Br2 (arXiv:2511.04475). It supports the analysis of correlated electronic structure, spectral properties, and local spin dynamics over a range of temperatures and band fillings. The dft_input directory includes Quantum ESPRESSO and Wannier90 input files used to construct the non-interacting electronic structure and low-energy Wannier Hamiltonians. The dmft-config-files folder contains DFT+DMFT configuration files for calculations performed with the TRIQS/solid_dmft framework. Raw outputs of the DMFT calculations are stored in dmft-output-files. Processed results, such as observables and the analytically continued spectral funciton, are collected in dmft-results. The plots directory provides the scripts used to generate the figures in the manuscript.
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Notes
| The data were generated using a combined density functional theory and dynamical mean-field theory (DFT+DMFT) approach. Density functional theory calculations were performed using a plane-wave pseudopotential method, followed by a Wannier90 construction of a Cu-centered low-energy subspace. A two-orbital model capturing the relevant Cu 3d states was employed for the correlated calculations. Local electronic correlations were treated within single-site DMFT using a rotationally invariant Hubbard–Kanamori interaction with parameters U and J as specified in the associated publication. The impurity problem was solved using a continuous-time quantum Monte Carlo (CT-HYB) solver. Calculations were carried out for multiple temperatures and a broad range of band fillings by adjusting the chemical potential self-consistently. Imaginary-frequency Green’s functions, self-energies, and local spin correlation functions were obtained directly from the DMFT simulations. Selected quantities, including spectral functions and correlated band structures, were derived via analytical continuation of the self-energy using maximum entropy methods. All post-processing and visualization were performed using custom Python scripts based on the TRIQS and solid_dmft libraries. |