TBHubbard: tight-binding and extended Hubbard model database 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 and quantum computing tasks become available. Here, we provide extensive simulation data augmentation for MOFs within the QMOF database. 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. The data supports computational workflows for identifying structure-property correlations that are needed for inverse material design. For validation and reuse, we have made the data available at https://dataverse.harvard.edu/dataverse/tbhubbard/.