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Register a new userAn Efficient DFT Solver for Nanoscale Simulations and Beyond
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We present the One-orbital Ensemble Self-Consistent Field (OE-SCF) method, an {alternative} orbital-free DFT solver that extends the applicability of DFT to system sizes beyond the nanoscale while retaining the accuracy required to be predictive. OE-SCF is an iterative solver where the (typically computationally expensive) Pauli potential is treated as an external potential and updated after each iteration. Because only up to a dozen iterations are needed to reach convergence, OE-SCF dramatically outperforms current orbital-free DFT solvers. Employing merely a single CPU, we carried out the largest ab initio simulation for silicon-based materials to date. OE-SCF is able to converge the energy of bulk-cut Si nanoparticles as a function of their diameter up to 16 nm, for the first time reproducing known empirical results. We model polarization and interface charge transfer when a Si slab is sandwiched between two metal slabs where lattice matching mandates a very large slab size. Additionally, OE-SCF opens the door to adopt even more accurate functionals in orbital-free DFT simulations while still tackling systems sizes beyond the nanoscale.
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In silico materials design is hampered by the computational complexity of Kohn-Sham DFT, which scales cubically with the system size. Owing to the development of new-generation kinetic energy density functionals (KEDFs), orbital-free DFT (OFDFT, a linear-scaling method) can now be successfully applied to a large class of semiconductors and such finite systems as quantum dots and metal clusters. In this work, we present DFTpy, an open source software implementing OFDFT written entirely in Python 3 and outsourcing the computationally expensive operations to third-party modules, such as NumPy and SciPy. When fast simulations are in order, DFTpy exploits the fast Fourier transforms (FFTs) from PyFFTW. New-generation, nonlocal and density-dependent-kernel KEDFs are made computationally efficient by employing linear splines and other methods for fast kernel builds. We showcase DFTpy by solving for the electronic structure of a million-atom system of aluminum metal which was computed on a single CPU. The Python 3 implementation is object-oriented, opening the door to easy implementation of new features. As an example, we present a time-dependent OFDFT implementation (hydrodynamic DFT) which we use to compute the spectra of small metal cluster recovering qualitatively the time-dependent Kohn-Sham DFT result. The Python code base allows for easy implementation of APIs. We showcase the combination of DFTpy and ASE for molecular dynamics simulations (NVT) of liquid metals. DFTpy is released under the MIT license.
By adopting a divide-and-conquer strategy, subsystem-DFT (sDFT) can dramatically reduce the computational cost of large-scale electronic structure calculations. The key ingredients of sDFT are the nonadditive kinetic energy and exchange-correlation functionals which dominate its accuracy. Even though, semilocal nonadditive functionals find a broad range of applications, their accuracy is somewhat limited especially for those systems where achieving balance between exchange-correlation interactions on one side and nonadditive kinetic energy on the other is crucial. In eQE 2.0, we improve dramatically the accuracy of sDFT simulations by (1) implementing nonlocal nonadditive kinetic energy functionals based on the LMGP family of functionals; (2) adapting Quantum ESPRESSOs implementation of rVV10 and vdW-DF nonlocal exchange-correlation functionals to be employed in sDFT simulations; (3) implementing deorbitalized meta GGA functionals (e.g., SCAN-L). We carefully assess the performance of the newly implemented tools on the S22-5 test set. eQE 2.0 delivers excellent interaction energies compared to conventional Kohn-Sham DFT and CCSD(T). The improved performance does not come at a loss of computational efficiency. We show that eQE 2.0 with nonlocal nonadditive functionals retains the same linear scaling behavior achieved in eQE 1.0 with semilocal nonadditive functionals.
Behaving like atomically-precise two-dimensional quantum wells with non-negligible dielectric contrast, the layered HOIPs have strong electronic interactions leading to tightly bound excitons with binding energies on the order of 500 meV. These strong interactions suggest the possibility of larger excitonic complexes like trions and biexcitons, which are hard to study numerically due to the complexity of the layered HOIPs. Here, we propose and parameterize a model Hamiltonian for excitonic complexes in layered HOIPs and we study the correlated eigenfunctions of trions and biexcitons using a combination of diffusion Monte Carlo and very large variational calculations with explicitly correlated Gaussian basis functions. Binding energies and spatial structures of these complexes are presented as a function of the layer thickness. The trion and biexciton of the thinnest layered HOIP have binding energies of 35 meV and 44 meV, respectively, whereas a single exfoliated layer is predicted to have trions and biexcitons with equal binding enegies of 48 meV. We compare our findings to available experimental data and to that of other quasi-two-dimensional materials.
High-level first-principles computations predict blue phosphorene bilayer to be a two-dimensional metal. This structure has not been considered before and was identified by employing a block-diagram scheme that yields the complete set of five high-symmetry stacking configurations of buckled honeycomb layers, and allows their unambiguous classification. We show that all of these stacking configurations are stable or at least metastable configurations both for blue phosphorene and gray arsenene bilayers. For blue phosphorene, the most stable stacking configuration has not yet been reported, and surprisingly it is metallic, while all other arrangements are indirect band gap semiconductors. As it is impossible to interchange the stacking configurations by translations, all of them should be experimentally accessible via the transfer of monolayers. The metallic character of blue phosphorene bilayer is caused by its short interlayer distance of 3.01 {AA} and offers the exceptional possibility to design single elemental all-phosphorus transistors.
In this manuscript, we outline a reliable procedure to manufacture photonic nanostructures from single-crystal diamond (SCD). Photonic nanostructures, in our case SCD nanopillars on thin (< 1$mu$m) platforms, are highly relevant for nanoscale sensing. The presented top-down procedure includes electron beam lithography (EBL) as well as reactive ion etching (RIE). Our method introduces a novel type of inter-layer, namely silicon, that significantly enhances the adhesion of hydrogen silsesquioxane (HSQ) electron beam resist to SCD and avoids sample charging during EBL. In contrast to previously used adhesion layers, our silicon layer can be removed using a highly-selective RIE step which is not damaging HSQ mask structures. We thus refine published nanofabrication processes to ease a higher process reliability especially in the light of the advancing commercialization of SCD sensor devices.
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