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

Nurhuda, M., Perry, C.C. ORCID: 0000-0003-1517-468X and Addicoat, M.A. ORCID: 0000-0002-5406-7927, 2022. Performance of GFN1-xTB for periodic optimization of metal organic frameworks. Physical Chemistry Chemical Physics, 24 (18), pp. 10906-10914. ISSN 1463-9076

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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 ca. 5000 atoms. 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 0.187 Å. 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.

Item Type: Journal article
Publication Title: Physical Chemistry Chemical Physics
Creators: Nurhuda, M., Perry, C.C. and Addicoat, M.A.
Publisher: Royal Society of Chemistry (RSC)
Date: 2022
Volume: 24
Number: 18
ISSN: 1463-9076
Rights: This journal is © the Owner Societies 2022. Open Access Article. This article is under a Creative Commons Attribution 3.0 Unported Licence.
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 25 Apr 2022 15:29
Last Modified: 24 Jun 2022 16:01

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