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

## TL;DR

This paper introduces a dataset of simulated electronic structures for metal-organic frameworks to support machine learning and quantum computing applications.

## Contribution

The paper provides a novel dataset with tight-binding and Hubbard model parameters for 10,000 MOFs, including self-consistent U and V values for transition metal-containing MOFs.

## Key findings

- A tight-binding representation was generated for 10,000 MOFs in the QMOF dataset.
- Extended Hubbard model parameters were computed for 240 MOFs containing transition metals.
- The dataset supports both machine learning and quantum computing applications.

## Abstract

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.