Materials Chemistry

Performance of GFN1-xTB for periodic optimization of Metal Organic Frameworks

Authors

Abstract

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 containing up to ca. 5000 atoms, and has an accuracy comparable to DFT but is 2-3 orders of magnitude faster. The efficiency of DFTB comes via 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 Z≤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 0.187Å. Therefore GFN-xTB provides the ability to calculate MOF structures more accurately than force fields, and ca. 2 orders of magnitude faster than DFT. We therefore propose that GFN-xTB is a suitable method for screening of hypothetical MOFs (Z ≤ 86), with the advantage of accurate binding energies for adsorption applications.

Content

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Supplementary material

Thumbnail image of SI-GFN_2022-01-12.docx
Supplementary methods and data
Supplementary methods and data
Thumbnail image of CoREDatabase_GFN_2022-01-12.xlsx
Index of GFN-xTB optimised CoRE structures
Spreadsheet containing index, RMSD and lattice parameters for all CoRE MOf structures. For failed optimisations, reason for failure is given.

Supplementary weblinks

GFN-xTB optimised CoRE MOF structures (cif format)
GFN-xTB optimised CoRE structures, with and without lattice optimisation.