Alejandro Lopez-Bezanilla

Boron Nitride Nanoribbons Become Metallic

Standard spin-polarized density functional theory calculations have been conducted to study the electronic structures and magnetic properties of O and S functionalized zigzag boron nitride nanoribbons (zBNNRs). Unlike the semiconducting and nonmagnetic H edge-terminated zBNNRs, the O edge-terminated zBNNRs have two energetically degenerate magnetic ground states with a ferrimagnetic character on the B edge, both of which are metallic. In contrast, the S edge-terminated zBNNRs are nonmagnetic albeit still metallic. An intriguing coexistence of two different Peierls-like distortions is observed for S edge-termination that manifests as a strong S dimerization at the B zigzag edge and a weak S trimerization at the N zigzag edge, dictated by the band fillings at the vicinity of the Fermi level. Nevertheless, metallicity is retained along the S wire on the N edge due to the partial filling of the band derived from the p(z) orbital of S. A second type of functionalization with O or S atoms embedded in the center of zBNNRs yields semiconducting features. Detailed examination of both types of functionalized zBNNRs reveals that the p orbitals on O or S play a crucial role in mediating the electronic structures of the ribbons. We suggest that O and S functionalization of zBNNRs may open new routes toward practical electronic devices based on boron nitride materials.

Effect of the chemical functionalization on charge transport in carbon nanotubes at the mesoscopic scale.

We present first-principles calculations of quantum transport in chemically functionalized metallic carbon nanotubes with lengths reaching the micrometer scale and random distributions of functional groups. Two typical cases are investigated, namely, a sp2-type bonding between carbene groups (CH2) and the nanotube sidewalls and a sp3-type bonding of nanotubes with paired phenyl groups. For similar molecular coverage density, charge transport is found to range from a quasi-ballistic-like to a strongly diffusive regime, with corresponding mean free paths changing by orders of magnitude depending on the nature of the chemical bonding.

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.

Chemical functionalization effects on armchair graphene nanoribbon transport.

We report first-principles transport calculations in chemically functionalized graphene nanoribbons. The effect of the joint attachment of hydroxyl and hydrogen groups on the graphene surface is investigated as a function of defect location and coverage density. The chemical bonding of a single defect pair (C-OH and C-H) is shown to considerably alter the conduction capability of ribbon channels, similarly to an sp(3) type of defect. With transport calculations in disordered ribbons with lengths up to the micrometer scale, the elastic mean free paths and conduction regimes are analyzed. Even in the low grafting density limit, transport properties are found to be severely damaged by the functionalization, indicating a strong tendency toward an insulating regime.

Machine learning for many-body physics: The case of the Anderson impurity model

Machine learning methods are applied to finding the Greens function of the Anderson impurity model, a basic model system of quantum many-body condensed-matter physics. Different methods of parametrizing the Greens function are investigated; a representation in terms of Legendre polynomials is found to be superior due to its limited number of coefficients and its applicability to state of the art methods of solution. The dependence of the errors on the size of the training set is determined. The results indicate that a machine learning approach to dynamical mean-field theory may be feasible.

Electronic properties of 8-Pmmn borophene

First-principles calculations on monolayer 8-{\it Pmmn} borophene are reported to reveal unprecedented electronic properties in a two-dimensional material. Based on a Born effective charge analysis, 8-{\it Pmmn} borophene is the first single-element based monolayered material exhibiting two sublattices with substantial ionic features. The observed Dirac cones are actually formed by the p

Modeling electronic quantum transport with machine learning

_z

Magnetism and Metal-Insulator Transition in Oxygen Deficient SrTiO3

orbitals of one of the inequivalent sublattices composed of uniquely four atoms, yielding an underlying hexagonal network topologically equivalent to distorted graphene. A significant physical outcome of this effect includes the possibility of converting metallic 8-{\it Pmmn} borophene into an indirect band gap semiconductor by means of external shear stress. The stability of the strained structures are supported by a phonon frequency analysis. The Dirac cones are sensitive to the formation of vacancies only in the inequivalent sublattice electronically active at the Fermi level.

Oxygen surface functionalization of graphene nanoribbons for transport gap engineering.

We present a machine learning approach to solve electronic quantum transport equations of one-dimensional nanostructures. The transmission coefficients of disordered systems were computed to provide training and test data sets to the machine. The systems representation encodes energetic as well as geometrical information to characterize similarities between disordered configurations, while the Euclidean norm is used as a measure of similarity. Errors for out-of-sample predictions systematically decrease with training set size, enabling the accurate and fast prediction of new transmission coefficients. The remarkable performance of our model to capture the complexity of interference phenomena lends further support to its viability in dealing with transport problems of undulatory nature.

Boron nitride materials: an overview from 0D to 3D (nano)structures: Boron nitride materials

First-principles calculations to study the electronic and magnetic properties of bulk, oxygen-deficient SrTiO3 (STO) under different doping conditions and densities have been conducted. The appearance of magnetism in oxygen-deficient STO is not determined solely by the presence of a single oxygen vacancy but by the density of free carriers and the relative proximity of the vacant sites. We find that while an isolated vacancy behaves as a nonmagnetic double donor, manipulation of the doping conditions allows the stability of a single-donor state, with emergent local moments coupled ferromagnetically by carriers in the conduction band. Strong local lattice distortions enhance the binding of this state. The energy of the in-gap local moment can be further tuned by orthorhombic strain. Consequently we find that the free-carrier density and strain are fundamental components to obtaining trapped spin-polarized electrons in oxygen-deficient STO, which may have important implications in the design of optical devices.