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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.
Given the widespread use of density functional theory (DFT), there is an increasing need for the ability to model large systems (beyond 1,000 atoms). We present a brief overview of the large-scale DFT code Conquest, which is capable of modelling such
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 importa
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 $GW$ ca
Linear scaling methods, or O(N) methods, have computational and memory requirements which scale linearly with the number of atoms in the system, N, in contrast to standard approaches which scale with the cube of the number of atoms. These methods, wh
We propose and analyze algorithms for distributionally robust optimization of convex losses with conditional value at risk (CVaR) and $chi^2$ divergence uncertainty sets. We prove that our algorithms require a number of gradient evaluations independe