Almost Perfect Spin Filtering in Graphene-Based Magnetic Tunnel Junctions.

We report on large spin-filtering effects in epitaxial graphene-based spin valves, strongly enhanced in our specific multilayer case. Our results were obtained by the effective association of chemical vapor deposited (CVD) multilayer graphene with a high quality epitaxial Ni(111) ferromagnetic spin source. We highlight that the Ni(111) spin source electrode crystallinity and metallic state are preserved and stabilized by multilayer graphene CVD growth. Complete nanometric spin valve junctions are fabricated using a local probe indentation process, and spin properties are extracted from the graphene-protected ferromagnetic electrode through the use of a reference Al2O3/Co spin analyzer. Strikingly, spin-transport measurements in these structures give rise to large negative tunnel magneto-resistance TMR = -160%, pointing to a particularly large spin polarization for the Ni(111)/Gr interface PNi/Gr, evaluated up to -98%. We then discuss an emerging physical picture of graphene-ferromagnet systems, sustained both by experimental data and ab initio calculations, intimately combining efficient spin filtering effects arising (i) from the bulk band structure of the graphene layers purifying the extracted spin direction, (ii) from the hybridization effects modulating the amplitude of spin polarized scattering states over the first few graphene layers at the interface, and (iii) from the epitaxial interfacial matching of the graphene layers with the spin-polarized Ni surface selecting well-defined spin polarized channels. Importantly, these main spin selection effects are shown to be either cooperating or competing, explaining why our transport results were not observed before. Overall, this study unveils a path to harness the full potential of low Resitance.Area (RA) graphene interfaces in efficient spin-based devices.

Spin filtering by proximity effects at hybridized interfaces in spin-valves with 2D graphene barriers.

We report on spin transport in state-of-the-art epitaxial monolayer graphene based 2D-magnetic tunnel junctions (2D-MTJs). In our measurements, supported by ab-initio calculations, the strength of interaction between ferromagnetic electrodes and graphene monolayers is shown to fundamentally control the resulting spin signal. In particular, by switching the graphene/ferromagnet interaction, spin transport reveals magneto-resistance signal MR > 80% in junctions with low resistance × area products. Descriptions based only on a simple K-point filtering picture (i.e. MR increase with the number of layers) are not sufficient to predict the behavior of our devices. We emphasize that hybridization effects need to be taken into account to fully grasp the spin properties (such as spin dependent density of states) when 2D materials are used as ultimately thin interfaces. While this is only a first demonstration, we thus introduce the fruitful potential of spin manipulation by proximity effect at the hybridized 2D material / ferromagnet interface for 2D-MTJs.

The ONETEP linear-scaling density functional theory program.

We present an overview of the onetep program for linear-scaling density functional theory (DFT) calculations with large basis set (plane-wave) accuracy on parallel computers. The DFT energy is computed from the density matrix, which is constructed from spatially localized orbitals we call Non-orthogonal Generalized Wannier Functions (NGWFs), expressed in terms of periodic sinc (psinc) functions. During the calculation, both the density matrix and the NGWFs are optimized with localization constraints. By taking advantage of localization, onetep is able to perform calculations including thousands of atoms with computational effort, which scales linearly with the number or atoms. The code has a large and diverse range of capabilities, explored in this paper, including different boundary conditions, various exchange-correlation functionals (with and without exact exchange), finite electronic temperature methods for metallic systems, methods for strongly correlated systems, molecular dynamics, vibrational calculations, time-dependent DFT, electronic transport, core loss spectroscopy, implicit solvation, quantum mechanical (QM)/molecular mechanical and QM-in-QM embedding, density of states calculations, distributed multipole analysis, and methods for partitioning charges and interactions between fragments. Calculations with onetep provide unique insights into large and complex systems that require an accurate atomic-level description, ranging from biomolecular to chemical, to materials, and to physical problems, as we show with a small selection of illustrative examples. onetep has always aimed to be at the cutting edge of method and software developments, and it serves as a platform for developing new methods of electronic structure simulation. We therefore conclude by describing some of the challenges and directions for its future developments and applications.

Universal Spin Diffusion Length in Polycrystalline Graphene.

Graphene grown by chemical vapor deposition (CVD) is the most promising material for industrial-scale applications based on graphene monolayers. It also holds promise for spintronics; despite being polycrystalline, spin transport in CVD graphene has been measured over lengths up to 30 µm, which is on par with the best measurements made in single-crystal graphene. These results suggest that grain boundaries (GBs) in CVD graphene, while impeding charge transport, may have little effect on spin transport. However, to date very little is known about the true impact of disordered networks of GBs on spin relaxation. Here, by using first-principles simulations, we derive an effective tight-binding model of graphene GBs in the presence of spin-orbit coupling (SOC), which we then use to evaluate spin transport in realistic morphologies of polycrystalline graphene. The spin diffusion length is found to be independent of the grain size, and it is determined only by the strength of the substrate-induced SOC. This result is consistent with the D'yakonov-Perel' mechanism of spin relaxation in the diffusive regime, but we find that it also holds in the presence of quantum interference. These results clarify the role played by GBs and demonstrate that the average grain size does not dictate the upper limit for spin transport in CVD-grown graphene, a result of fundamental importance for optimizing large-scale graphene-based spintronic devices.

Anharmonic Infrared Spectroscopy through the Fourier Transform of Time Correlation Function Formalism in ONETEP.

Density functional theory molecular dynamics (DFT-MD) provides an efficient framework for accurately computing several types of spectra. The major benefit of DFT-MD approaches lies in the ability to naturally take into account the effects of temperature and anharmonicity, without having to introduce any ad hoc or a posteriori corrections. Consequently, computational spectroscopy based on DFT-MD approaches plays a pivotal role in the understanding and assignment of experimental peaks and bands at finite temperature, particularly in the case of floppy molecules. Linear-scaling DFT methods can be used to study large and complex systems, such as peptides, DNA strands, amorphous solids, and molecules in solution. Here, we present the implementation of DFT-MD IR spectroscopy in the ONETEP linear-scaling code. In addition, two methods for partitioning the dipole moment within the ONETEP framework are presented. Dipole moment partitioning allows us to compute spectra of molecules in solution, which fully include the effects of the solvent, while at the same time removing the solvent contribution from the spectra.

Achievements of DFT for the investigation of graphene-related nanostructures.

CONSPECTUS: Graphene-related nanostructures stand out as exceptional materials due to both their wide range of properties and their expanse of interest in both applied and fundamental research. They are good examples of nanoscale materials for which the properties do not necessarily replicate those of the bulk. For the description and the understanding of their properties, it is clear that a general quantum-mechanical approach is mandatory. The remarkable result of density functional theory (DFT) is that the quantum-mechanical description of materials at the ground state is made amenable to simulations at a relatively low computational cost. The knowledge of materials has undergone a revolution after the introduction of DFT as an unrivaled instrument for the investigation of materials properties through computer experiments. Their deeper understanding comes from a variety of tools developed from concepts intrinsically present in DFT, notably the total energy and the charge density. Such tools allow the prediction of a diverse set of physicochemical properties relevant for material scientists. This Account lays out an example-driven tour through the achievements of ground-state DFT applied to the description of graphene-related nanostructures and to the deep understanding of their outstanding properties. After a brief introduction to DFT, the survey starts with the determination of the most basic properties that can be obtained from DFT, that is, band structures, lattice parameters, and spin ground state. Next follows an exploration of how total energies of different systems can give information about relative stability, formation energies, and reaction paths. Exploiting the derivatives of the energy with respect to displacements leads the way toward the extraction of vibrational and mechanical properties. In addition, a close examination of the charge density gives information about charge transfer mechanisms, which can be linked to chemical reactivity. The ground state density and Hamiltonian finally connect to the concepts behind transport phenomena, which drive much of the application-oriented research on graphene and graphene-related nanostructures. In each section, a selection of cases that are of current importance are used to illustrate the use and relevance of DFT-based techniques. In summary, this Account presents an introductory landscape of the possibilities of ground-state DFT for the study of graphene-related nanostructures. The prospect is rich, and the use of DFT for the study of graphene-related nanostructures will continue to be fruitful for the advancement of these and other materials.

Two-dimensional graphene with structural defects: elastic mean free path, minimum conductivity, and Anderson transition.

Quantum transport properties of disordered graphene with structural defects (Stone-Wales and divacancies) are investigated using a realistic π-π* tight-binding model elaborated from ab initio calculations. Mean free paths and semiclassical conductivities are then computed as a function of the nature and density of defects (using an order-N real-space Kubo-Greenwood method). By increasing the defect density, the decay of the semiclassical conductivities is predicted to saturate to a minimum value of 4e2/πh over a large range (plateau) of carrier density (>0.5×10(14) cm(-20). Additionally, strong contributions of quantum interferences suggest that the Anderson localization regime could be experimentally measurable for a defect density as low as 1%.

Quantum transport in graphene nanoribbons: effects of edge reconstruction and chemical reactivity.

We present first-principles transport calculations of graphene nanoribbons with chemically reconstructed edge profiles. Depending on the geometry of the defect and the degree of hydrogenation, spectacularly different transport mechanisms are obtained. In the case of monohydrogenated pentagon (heptagon) defects, an effective acceptor (donor) character results in strong electron-hole conductance asymmetry. In contrast, weak backscattering is obtained for defects that preserve the benzenoid structure of graphene. Based on a tight-binding model derived from ab initio calculations, evidence for large conductance scaling fluctuations are found in disordered ribbons with lengths up to the micrometer scale.