TBHubbard: tight-binding and extended Hubbard model dataset for metal-organic frameworks

Metal-organic frameworks (MOFs) are porous materials composed of metal ions and organic linkers. Due to their chemical diversity, MOFs can support a broad range of applications in chemical separations. However, the vast amount of structural compositions encoded in crystallographic information files complicates application-oriented computational screening and design. The existing crystallographic data, therefore, requires augmentation by simulated data so that suitable descriptors for machine-learning tasks become available. Here, we provide extensive simulation data augmentation for MOFs within the QMOF dataset. We have applied a tight-binding, lattice Hamiltonian and density functional theory to MOFs for performing electronic structure calculations. Specifically, we provide a tight-binding representation of 10,000 MOFs, and an Extended Hubbard model representation for a sub-set of 240 MOFs containing transition metals, where intra-site U and inter-site V parameters are computed self-consistently. In addition to computational workflows for identifying structure-property correlations, the data supports quantum computing tasks that rely on tight-binding Hamiltonian and self-consistent computed Hubbard parameters. For validation and reuse, we have made the data publicly available.