A1 Journal article (refereed)
Optimizing density-functional simulations for two-dimensional metals (2022)

Abidi, K. R., & Koskinen, P. (2022). Optimizing density-functional simulations for two-dimensional metals. Physical review materials, 6(12), Article 124004. https://doi.org/10.1103/physrevmaterials.6.124004

JYU authors or editors

Publication details

All authors or editorsAbidi, Kameyab Raza; Koskinen, Pekka

Journal or seriesPhysical review materials



Publication year2022

Publication date27/12/2022


Issue number12

Article number124004

PublisherAmerican Physical Society (APS)

Publication countryUnited States

Publication languageEnglish


Publication open accessOpenly available

Publication channel open accessOpen Access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/84784


Unlike covalent two-dimensional (2D) materials like graphene, 2D metals have nonlayered structures due to their nondirectional, metallic bonding. While experiments on 2D metals are still scarce and challenging, density-functional theory (DFT) provides an ideal approach to predict their basic properties and assist in their design. However, DFT methods have rarely been benchmarked against metallic bonding at low dimensions. Therefore, to identify optimal DFT attributes for a desired accuracy, we systematically benchmark exchange-correlation functionals from LDA to hybrids and basis sets from plane waves to local basis with different pseudopotentials. With 1D chain, 2D honeycomb, 2D square, 2D hexagonal, and 3D bulk metallic systems, we compare the DFT attributes using bond lengths, cohesive energies, elastic constants, densities of states, and computational costs. Although today most DFT studies on 2D metals use plane waves, our comparisons reveal that local basis with often-used Perdew-Burke-Ernzerhof exchange correlation is quite sufficient for most purposes, while plane waves and hybrid functionals bring limited improvement compared to the greatly increased computational cost. These results ease the demands for generating DFT data for better interaction with experiments and for data-driven discoveries of 2D metals incorporating machine learning algorithms.

Keywordschemical bondsdensityelasticity (physical properties)

Free keywordschemical bonding; density of states; elasticity

Contributing organizations

Ministry reportingYes

Reporting Year2022

JUFO rating2

Last updated on 2024-15-06 at 22:06