Low-Cost Periodic Calculations of Metal-Organic Frameworks : A GFN1-xTB Perspective

Title data

Pokora, Mateusz ; Goclon, Jakub ; Margraf, Johannes T. ; Panosetti, Chiara ; Samtsevych, Artem ; Paneth, Piotr:
Low-Cost Periodic Calculations of Metal-Organic Frameworks : A GFN1-xTB Perspective.
In: ChemPhysChem. Vol. 26 (2025) Issue 14 . - e202500081.
ISSN 1439-7641
DOI: https://doi.org/10.1002/cphc.202500081

Official URL:

Abstract in another language

Semiempirical extended tight-binding (GFN1-xTB) and semilocal density functional theory (DFT)(Perdew–Becke–Ernzerhof (PBE)+D3) calculations are performed to evaluate the structural and electronic properties of five metal-organic frameworks (MOFs): rigid MOF-5(Zn), IRMOF(II)-74(Mg), ZIF-8(Zn), and flexible MIL-53(Al) and MIL-53(Fe). It is found that GFN1-xTB exhibits a similar performance to that of DFT in terms of accuracy of lattice vector preservation. Structural integrity is further supported by the low average root-mean-square displacement (RMSD) of the atomic positions, which remains below 0.3 Å. Consequently, the textural properties are also well preserved by GFN1-xTB, showing good agreement with those obtained from DFT. GFN1-xTB molecular dynamics (MD) simulations exhibit structural stability and correctly predict structural responses to temperature, which is fully consistent with experimental results. In addition, based on MD trajectories, this study constructs time-averaged X-ray diffraction patterns that closely aligned with experimental data. More importantly, GFN1-xTB performs exceptionally well at predicting the bandgap. Overall, GFN1-xTB offers almost semilocal DFT accuracy with significantly higher computational efficiency, making it a valuable tool for describing the geometric, textural, dynamic, and selected electronic properties of MOFs.

Further data

Item Type:	Article in a journal
Refereed:	Yes
Keywords:	density of states; density functional theory; GFN1-xTB; metal-organic frameworks; molecular dynamics
Institutions of the University:	Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Physical Chemistry V - Theory and Machine Learning > Chair Physical Chemistry V - Theory and Machine Learning - Univ.-Prof. Dr. Johannes Theo Margraf
Result of work at the UBT:	Yes
DDC Subjects:	500 Science > 540 Chemistry
Date Deposited:	10 Nov 2025 09:49
Last Modified:	10 Nov 2025 09:49
URI:	https://eref.uni-bayreuth.de/id/eprint/95161