Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userCombined sub-sampling and analytical integration for efficient large-scale $GW$ calculations for 2D systems
129
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Accurate and efficient predictions of the quasiparticle properties of complex materials remain a major challenge due to the convergence issue and the unfavorable scaling of the computational cost with respect to the system size. Quasiparticle $GW$ calculations for two dimensional (2D) materials are especially difficult. The unusual analytical behaviors of the dielectric screening and the electron self-energy of 2D materials make the conventional Brillouin zone (BZ) integration approach rather inefficient and require an extremely dense $k$-grid to properly converge the calculated quasiparticle energies. In this work, we present a combined non-uniform sub-sampling and analytical integration method that can drastically improve the efficiency of the BZ integration in 2D $GW$ calculations. Our work is distinguished from previous work in that, instead of focusing on the intricate dielectric matrix or the screened Coulomb interaction matrix, we exploit the analytical behavior of various terms of the convolved self-energy $Sigma(mathbf{q})$ in the small $mathbf{q}$ limit. This method, when combined with another accelerated $GW$ method that we developed recently, can drastically speed-up (by over three orders of magnitude) $GW$ calculations for 2D materials. Our method allows fully converged $GW$ calculations for complex 2D systems at a fraction of computational cost, facilitating future high throughput screening of the quasiparticle properties of 2D semiconductors for various applications. To demonstrate the capability and performance of our new method, we have carried out fully converged $GW$ calculations for monolayer C$_2$N, a recently discovered 2D material with a large unit cell, and investigate its quasiparticle band structure in detail.
rate research
Read More
The search for new materials, based on computational screening, relies on methods that accurately predict, in an automatic manner, total energy, atomic-scale geometries, and other fundamental characteristics of materials. Many technologically important material properties directly stem from the electronic structure of a material, but the usual workhorse for total energies, namely density-functional theory, is plagued by fundamental shortcomings and errors from approximate exchange-correlation functionals in its prediction of the electronic structure. At variance, the $GW$ method is currently the state-of-the-art {em ab initio} approach for accurate electronic structure. It is mostly used to perturbatively correct density-functional theory results, but is however computationally demanding and also requires expert knowledge to give accurate results. Accordingly, it is not presently used in high-throughput screening: fully automatized algorithms for setting up the calculations and determining convergence are lacking. In this work we develop such a method and, as a first application, use it to validate the accuracy of $G_0W_0$ using the PBE starting point, and the Godby-Needs plasmon pole model ($G_0W_0^textrm{GN}$@PBE), on a set of about 80 solids. The results of the automatic convergence study utilized provides valuable insights. Indeed, we find correlations between computational parameters that can be used to further improve the automatization of $GW$ calculations. Moreover, we find that $G_0W_0^textrm{GN}$@PBE shows a correlation between the PBE and the $G_0W_0^textrm{GN}$@PBE gaps that is much stronger than that between $GW$ and experimental gaps. However, the $G_0W_0^textrm{GN}$@PBE gaps still describe the experimental gaps more accurately than a linear model based on the PBE gaps.
We present a method to efficiently combine the computation of electron-electron and electron-phonon self-energies, which enables the evaluation of electron-phonon coupling at the $G_0W_0$ level of theory for systems with hundreds of atoms. In addition, our approach, which is a generalization of a method recently proposed for molecules [J. Chem. Theory Comput. 2018, 14, 6269-6275], enables the inclusion of non-adiabatic and temperature effects at no additional computational cost. We present results for diamond and defects in diamond and discuss the importance of numerically accurate $G_0W_0$ band structures to obtain robust predictions of zero point renormalization (ZPR) of band gaps, and of the inclusion of non-adiabatic effect to accurately compute the ZPR of defect states in the band gap.
We develop a method to efficiently construct phase diagrams using machine learning. Uncertainty sampling (US) in active learning is utilized to intensively sample around phase boundaries. Here, we demonstrate constructions of three known experimental phase diagrams by the US approach. Compared with random sampling, the US approach decreases the number of sampling points to about 20%. In particular, the reduction rate is pronounced in more complicated phase diagrams. Furthermore, we show that using the US approach, undetected new phase can be rapidly found, and smaller number of initial sampling points are sufficient. Thus, we conclude that the US approach is useful to construct complicated phase diagrams from scratch and will be an essential tool in materials science.
One of the goals in the development of large scale electronic structure methods is to perform calculations explicitly for a localised region of a system, while still taking into account the rest of the system outside of this region. An example of this in surface physics would be to embed an adsorbate and a few surface atoms into an extended substrate, hence considerably reducing computational costs. Here we apply the constrained electron density method of embedding a Kohn-Sham system in a substrate system (first described by P. Cortonacite{1} and T.A. Wesolowskicite{2}), within a plane-wave basis and pseudopotential framework. This approach divides the charge density of the system into substrate and embedded charge densities, the sum of which is the charge density of the actual system of interest. Two test cases are considered. First we construct fcc bulk aluminium by embedding one cubic lattice of atoms within another. Second, we examine a model surface/adsorbate system of aluminium on aluminium and compare with full Kohn-Sham results.
In the context of photoelectron spectroscopy, the $GW$ approach has developed into the method of choice for computing excitation spectra of weakly correlated bulk systems and their surfaces. To employ the established computational schemes that have been developed for three-dimensional crystals, two-dimensional systems are typically treated in the repeated-slab approach. In this work we critically examine this approach and identify three important aspects for which the treatment of long-range screening in two dimensions differs from the bulk: (1) anisotropy of the macroscopic screening (2) $mathbf k$-point sampling parallel to the surface (3) periodic repetition and slab-slab interaction. For prototypical semiconductor (silicon) and ionic (NaCl) thin films we quantify the individual contributions of points (1) to (3) and develop robust and efficient correction schemes derived from the classic theory of dielectric screening.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
