Small basis set density functional theory method for cost-efficient, large-scale condensed matter simulations

DOI zum Zitieren der Version auf EPub Bayreuth: https://doi.org/10.15495/EPub_UBT_00008185
URN to cite this document: urn:nbn:de:bvb:703-epub-8185-9

Title data

Keller, Elisabeth ; Morgenstein, Jack ; Reuter, Karsten ; Margraf, Johannes T.:
Small basis set density functional theory method for cost-efficient, large-scale condensed matter simulations.
In: The Journal of Chemical Physics. Vol. 161 (2024) Issue 7 . - 074104.
ISSN 0021-9606
DOI der Verlagsversion: https://doi.org/10.1063/5.0222649

	Format: PDF Name: 074104_1_5.0222649.pdf Version: Published Version Available under License Creative Commons BY 4.0: Attribution
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Abstract

We present an efficient first-principles based method geared toward reliably predicting the structures of solid materials across the Periodic Table. To this end, we use a density functional theory baseline with a compact, near-minimal min+s basis set, yielding low computational costs and memory demands. Since the use of such a small basis set leads to systematic errors in chemical bond lengths, we develop a linear pairwise correction, available for elements Z = 1–86 (excluding the lanthanide series), parameterized for use with the Perdew–Burke–Ernzerhof exchange–correlation functional. We demonstrate the reliability of this corrected approach for equilibrium volumes across the Periodic Table and the transferability to differently coordinated environments and multi-elemental crystals. We examine relative energies, forces, and stresses in geometry optimizations and molecular dynamics simulations.

Further data

Item Type:	Article in a journal
DDC Subjects:	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-8185-9
Date Deposited:	11 Feb 2025 06:34
Last Modified:	11 Feb 2025 06:34
URI:	https://epub.uni-bayreuth.de/id/eprint/8185

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