Exploring atom-pairwise and many-body dispersion corrections for the BEEF-vdW functional

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00008247
URN to cite this document: urn:nbn:de:bvb:703-epub-8247-4

Title data

Keller, Elisabeth ; Blum, Volker ; Reuter, Karsten ; Margraf, Johannes T.:
Exploring atom-pairwise and many-body dispersion corrections for the BEEF-vdW functional.
In: The Journal of Chemical Physics. Vol. 162 (2025) Issue 7 . - 074111.
ISSN 0021-9606
DOI der Verlagsversion: https://doi.org/10.1063/5.0248728

	Format: PDF Name: 074111_1_5.0248728.pdf Version: Published Version Available under License Creative Commons BY 4.0: Attribution
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Abstract

The Bayesian error estimation functional (BEEF-vdW) is widely used in surface science and catalysis, because it provides a balanced description of molecular, surface, and solid state systems, along with reliable error estimates. However, the nonlocal van-der-Waals density functional (vdW-DF2) employed in BEEF-vdW can be computationally costly and displays relatively low accuracy for molecular systems. Therefore, this work explores whether atom-pairwise and many-body dispersion treatments represent viable alternatives to using the vdW-DF2 functional with BEEF-vdW. To this end, we investigate the performance of commonly used atom-pairwise corrections i.e., the Tkatchenko–Scheffler (TS) and the exchange-hole dipole moment (XDM) approaches and many-body dispersion (MBD) treatments for molecular, surface, and solid-state systems. The results indicate that atom-pairwise methods such as TS and particularly XDM provide a good balance of cost and accuracy across all systems.

Further data

Item Type:	Article in a journal
Keywords:	Density functional theory; Bayesian error estimation
DDC Subjects:	500 Science > 500 Natural sciences 500 Science > 540 Chemistry
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 Faculties Faculties > Faculty of Biology, Chemistry and Earth Sciences Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry
Language:	English
Originates at UBT:	Yes
URN:	urn:nbn:de:bvb:703-epub-8247-4
Date Deposited:	27 Feb 2025 09:08
Last Modified:	27 Feb 2025 09:08
URI:	https://epub.uni-bayreuth.de/id/eprint/8247

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