  Unlike covalent two-dimensional (2D) materials like graphene, 2D metals have
non-layered structures due to their non-directional, 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 been rarely 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 PBE exchange-correlation is well 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.
