Performance of GFN1-xTB for periodic optimization of metal organic frameworks.

Tight-binding approaches bridge the gap between force field methods and Density Functional Theory (DFT). Density Functional Tight Binding (DFTB) has been employed for a wide range of systems including proteins, clays and 2D and 3D materials. DFTB is 2-3 orders of magnitude faster than DFT, allowing calculations containing up to 5000 atoms. The efficiency of DFTB comes pre-computed integrals, which are parameterized for each pair of atoms, and the requirement for this parameterization has previously prevented widespread use of DFTB for Metal-Organic Frameworks. The GFN-xTB (Geometries, Frequencies, and Non-covalent interactions Tight Binding) method provides parameters for elements up to ≤ 86. We have therefore employed GFN-xTB to periodic optimizations of the Computation Ready Experimental (CoRE) database of MOF structures. We find that 75% of all cell parameters remain within 5% of the reference (experimental) value and that bonds containing metal atoms are typically well conserved with a mean average deviation of 0.187 Å. Therefore GFN-xTB provides the ability to calculate MOF structures more accurately than force fields, and 2 orders of magnitude faster than DFT. We therefore propose that GFN-xTB is a suitable method for screening of hypothetical MOFs ( ≤ 86), with the advantage of accurate binding energies for adsorption applications.