Recent progress of the Computational 2D Materials Database (C2DB)

Abstract

The Computational 2D Materials Database (C2DB) is a highly curated open database organising a wealth of computed properties for more than 4000 atomically thin two-dimensional (2D) materials. Here we report on new materials and properties that were added to the database since its first release in 2018. The set of new materials comprise several hundred monolayers exfoliated from experimentally known layered bulk materials, (homo)bilayers in various stacking configurations, native point defects in semiconducting monolayers, and chalcogen/halogen Janus monolayers. The new properties include exfoliation energies, Bader charges, spontaneous polarisations, Born charges, infrared polarisabilities, piezoelectric tensors, band topology invariants, exchange couplings, Raman spectra and second harmonic generation spectra. We also describe refinements of the employed material classification schemes, upgrades of the computational methodologies used for property evaluations, as well as significant enhancements of the data documentation and provenance. Finally, we explore the performance of Gaussian process-based regression for efficient prediction of mechanical and electronic materials properties. The combination of open access, detailed documentation, and extremely rich materials property data sets make the C2DB a unique resource that will advance the science of atomically thin materials.

Representing individual electronic states for machine learning GW band structures of 2D materials

“…Since the ML model can be used to calculate G 0 W 0 energies at any k -point grid, it is possible to use the method to calculate effective masses. Effective masses at the valence and conduction band extrema can be calculated by fitting a second-order polynomial to the energies at a densely sampled k -point grid centered around the band extrema 20 , 21 . This method is generally challenging with G 0 W 0 due to the high computational cost of calculating the energies at sufficiently dense k -point grids, but using the ML model it is possible to achieve accurate estimates of the G 0 W 0 effective masses.…”

“…The data set comprises quasiparticle (QP) energies from 286 G 0 W 0 band structures of non-magnetic 2D semiconductors covering 14 different crystal structures and 52 chemical elements. The QP energies have been obtained from plane-wave-based one-shot G 0 W 0 @PBE calculations with full frequency integration and were produced as a part of the Computational 2D Materials Database (C2DB) 20 , 21 . The data set has been described and analysed in detail in ref.…”

“…We have used the resulting ML model to obtain G 0 W 0 band structures for ∼700 2D semiconductors from the Computational 2D Materials Database (C2DB) 20 , 21 . These materials are additional to the data set used in this study, and the band structures will be published on the C2DB web page 22 .…”

Length-Gauge Optical Matrix Elements in WIEN2k

“…Due to large excitonic effects [30], direct comparison of the YSH band gap E YSH g = 2.22 eV with experiment was not possible. Thus, we used a many-body result E G 0 W 0 g = 2.53 eV [31,32] as a reference. Similarly to other materials, the monolayer MoS 2 showed strong enhancement of the matrix elements (Table 5) with the YSH XC functional.…”

Spread-balanced Wannier functions: Robust and automatable orbital localization

“…We have compared the calculated spontaneous polarization of tetragonal BaTiO 3 obtained with a direct implementation of the Berry phase method [18] with that obtained from Eq. ( 9) using the var spread functional Eq.…”