Nanoribbons of molybdenum disulfide (MoS2) are interesting one-dimensional (1D) nanostructures with intriguing electronic properties, consisting of a semiconducting bulk bounded by edges with metallic character. Edges of similar character can also be expected in other transition-metal dichalcogenide (TMDC) nanostructures. We report first-principles electronic structure calculations for the total energy and the band structure of four representative TMDCs, MoS2, MoSe2, WS2, WSe2, in various 1D nanoribbon configurations. We compare the thermodynamic stability and the electronic structure of the 2D bulk and 35 different quasi-1D nanoribbons for each of the four materials. In each case, we consider the reconstructions of the zigzag metal-terminated edge by adding different amounts of chalcogen adatoms. The 1D structures we investigated have positive edge energies when the chalcogen chemical potential is close to the energy of the bulk chalcogen phase, and negative edge energies for higher chemical potential values. We find that the reconstruction with two chalcogen adatoms per edge metal atom is the most stable under usual experimental conditions and that all 1D nanoribbon structures exhibit metallic character.

Small polaron formation in transition metal oxides, like the prototypical material rutile TiO2, remains a puzzle and a challenge to simple theoretical treatment. In our combined experimental and theoretical study, we examine this problem using Raman spectroscopy of photoexcited samples and real-time time-dependent density functional theory (RT-TDDFT), which employs Ehrenfest dynamics to couple the electronic and ionic subsystems. We observe experimentally the unexpected stiffening of the A(1g) phonon mode under UV illumination and provide a theoretical explanation for this effect. Our analysis also reveals a possible reason for the observed anomalous temperature dependence of the Hall mobility. Small polaron formation in rutile TiO2 is a strongly nonadiabatic process and is adequately described by Ehrenfest dynamics at time scales of polaron formation.

Iodine-doped graphene has recently attracted significant interest as a result of its enhanced conductivity and improved catalytic activity. Using density functional theory calculations, we obtain the formation energy, desorption rate, and electronic properties for graphene systems doped with polyiodide chains consisting of 1-6 iodine atoms in the low-concentration limit. We find that I-3 and I-5 act as p-type surface dopants that shift the Fermi level 0.46 and 0.57 eV below the Dirac point, respectively. For these two molecules, molecular orbital theory and analysis of the charge density show that doping transfers electronic charge to iodine pi* molecular orbitals oriented perpendicular to the graphene sheet. For even-length polyiodides, we find that I-6 and I-4 decompose to I-2, which readily desorbs at 300 K. Adsorption energy calculations further show that I-3 acts as an effective catalyst for the oxygen reduction reaction on graphene by stabilizing the rate-limiting OOH intermediate.

The graphene/MoS2 heterojunction formed by joining the two components laterally in a single plane promises to exhibit a low-resistance contact according to the Schottky-Mott rule. Here we provide an atomic-scale description of the structural, electronic, and magnetic properties of this type of junction. We first identify the energetically favorable structures in which the preference of forming C-S or C-Mo bonds at the boundary depends on the chemical conditions. We find that significant non-carrier related charge transfer between graphene and undoped MoS2 is localized at the boundary. We show that the abundant 1D boundary states substantially pin the Fermi level in the lateral contact between graphene and MoS2, in close analogy to the effect of 2D interfacial states in the contacts between 3D materials. Furthermore, we propose specific ways in which these effects can be exploited to achieve spin-polarized currents.

The dynamical glass transition is typically taken to be the temperature at which a glassy liquid is no longer able to equilibrate on experimental timescales. Consequently, the physical properties of these systems just above or below the dynamical glass transition, such as viscosity, can change by many orders of magnitude over long periods of time following external perturbation. During this progress toward equilibrium, glassy systems exhibit a history dependence that has complicated their study. In previous work, we bridged the gap between structure and dynamics in glassy liquids above their dynamical glass transition temperatures by introducing a scalar field called ``softness,'' a quantity obtained using machine-learning methods. Softness is designed to capture the hidden patterns in relative particle positions that correlate strongly with dynamical rearrangements of particle positions. Here we show that the out-of-equilibrium behavior of a model glass-forming system can be understood in terms of softness. To do this we first demonstrate that the evolution of behavior following a temperature quench is a primarily structural phenomenon: The structure changes considerably, but the relationship between structure and dynamics remains invariant. We then show that the relaxation time can be robustly computed from structure as quantified by softness, with the same relation holding both in equilibrium and as the system ages. Together, these results show that the history dependence of the relaxation time in glasses requires knowledge only of the softness in addition to the usual state variables.

Many structural and mechanical properties of crystals, glasses, and biological macromolecules can be modeled from the local interactions between atoms. These interactions ultimately derive from the quantum nature of electrons, which can be prohibitively expensive to simulate. Machine learning has the potential to revolutionize materials modeling due to its ability to efficiently approximate complex functions. For example, neural networks can be trained to reproduce results of density functional theory calculations at a much lower cost. However, how neural networks reach their predictions is not well understood, which has led to them being used as a ``black box'' tool. This lack of understanding is not desirable especially for applications of neural networks in scientific inquiry. We argue that machine learning models trained on physical systems can be used as more than just approximations since they had to ``learn'' physical concepts in order to reproduce the labels they were trained on. We use dimensionality reduction techniques to study in detail the representation of silicon atoms at different stages in a neural network, which provides insight into how a neural network learns to model atomic interactions. Published by AIP Publishing.

We investigate the behavior of sulfur vacancy defects, the most abundant type of intrinsic defect in monolayer MoS2, using first-principles calculations based on density functional theory. We consider the dependence of the isolated defect formation energy on the charge state and on uniaxial tensile and compressive strain up to 5%. We also consider the possibility of defect clustering by examining the formation energies of pairs of vacancies at various relative positions, and their dependence on charge state and strain. We find that there is no driving force for vacancy clustering, independent of strain in the material. The barrier for diffusion of S vacancies is larger than 1.9 eV in both charged and neutral states regardless of the presence of other nearby vacancies. We conclude that the formation of extended defects from S vacancies in planar monolayer MoS2 is hindered both thermodynamically and kinetically.

The ability in experiments to control the relative twist angle between successive layers in two-dimensional (2D) materials offers an approach to manipulating their electronic properties; we refer to this approach as ``twistronics.'' A major challenge to theory is that, for arbitrary twist angles, the resulting structure involves incommensurate (aperiodic) 2D lattices. Here, we present a general method for the calculation of the electronic density of states of aperiodic 2D layered materials, using parameter-free Hamiltonians derived from ab initio density-functional theory. We use graphene, a semimetal, and MoS2, a representative of the transition-metal dichalcogenide family of 2D semiconductors, to illustrate the application of our method, which enables fast and efficient simulation of multilayered stacks in the presence of local disorder and external fields. We comment on the interesting features of their density of states as a function of twist angle and local configuration and on how these features can be experimentally observed.

We use scanning tunneling microscopy (STM) and quasiparticle interference (QPI) imaging to investigate the low-energy orbital texture of single-layer FeSe/SrTiO3. We develop a T-matrix model of multiorbital QPI to disentangle scattering intensities from Fe 3d(xz) and 3d(yz) bands, enabling the use of STM as a nanoscale detection tool of nematicity. By sampling multiple spatial regions of a single-layer FeSe/SrTiO3 film, we quantitatively exclude static xz/yz orbital ordering with domain size larger than delta r(2) = 20 nm x 20 nm, xz/yz Fermi wave vector difference larger than delta k = 0.014 pi, and energy splitting larger than delta E = 3.5 meV. The lack of detectable ordering pinned around defects places qualitative constraints on models of fluctuating nematicity.

Decreasing energy consumption in the production of platform chemicals is necessary to improve the sustainability of the chemical industry, which is the largest consumer of delivered energy. The majority of industrial chemical transformations rely on catalysts, and therefore designing new materials that catalyse the production of important chemicals via more selective and energy-efficient processes is a promising pathway to reducing energy use by the chemical industry. Efficiently designing new catalysts benefits from an integrated approach involving fundamental experimental studies and theoretical modelling in addition to evaluation of materials under working catalytic conditions. In this review, we outline this approach in the context of a particular catalyst-nanoporous gold (npAu)-which is an unsupported, dilute AgAu alloy catalyst that is highly active for the selective oxidative transformation of alcohols. Fundamental surface science studies on Au single crystals and AgAu thin-film alloys in combination with theoretical modelling were used to identify the principles which define the reactivity of npAu and subsequently enabled prediction of new reactive pathways on this material. Specifically, weak van der Waals interactions are key to the selectivity of Au materials, including npAu. We also briefly describe other systems in which this integrated approach was applied.

One of the most critical factors in oxidation catalysis is controlling the state of oxygen on the surface. Au and Ag are both effective selective oxidation catalysts for various reactions, and their interactions with oxygen are critical for determining their catalytic performance. Here, we show that the state of oxygen on a catalytic surface can be controlled by alloying Au and Ag. Using temperature programmed desorption, density functional theory (DFT), and Monte Carlo simulations, we examine how alloying Au into an Ag(110) surface affects O-2 dissociation, O coverage, and O stability. DFT calculations indicate that Au resides in the second layer, in agreement with previous experimental findings. The minimum ensemble size for O-2 dissociation is 2 Ag atoms in adjacent rows of the second layer. Surprisingly, adsorbed O-2 and the dissociation transition state interact directly with metal atoms in the adjacent trough, such that Au in this position inhibits O-2 dissociation by direct repulsion with oxygen electronic states. Using Monte Carlo simulations based on DFT energetics, we create models of the surface that agree closely with our experimental results. Both show that the O-2 uptake decreases nearly linearly as the Au concentration increases, and no O-2 uptake occurs for Au concentrations above 50%. For Au concentrations greater than 18%, increasing the Au concentration also decreases the stability of the adsorbed O. Based on these results, the O coverage and O stability can be tuned, in some cases independently. We also study how the reactivity of the surface is affected by these factors using CO2 oxidation as a simple test reaction.

The Au(110) surface offers unique advantages for atomically-resolved model studies of catalytic oxidation processes on gold. We investigate the adsorption of oxygen on Au(110) using a combination of scanning tunneling microscopy (STM) and density functional theory (DFT) methods. We identify the typical (empty-states) STM contrast resulting from adsorbed oxygen as atomic-sized dark features of electronic origin. DFT-based image simulations confirm that chemisorbed oxygen is generally detected indirectly, from the binding -induced electronic structure modification of gold. STM images show that adsorption occurs without affecting the general structure of the pristine Au(110) missing-row reconstruction. The tendency to form one-dimensional structures is observed already at low coverage (<0.05 ML), with oxygen adsorbing on alternate sides of the reconstruction ridges. Consistently, calculations yield preferred adsorption on the (111) facets of the reconstruction, on a 3-fold coordination site, with increased stability when adsorbed in chains. Gold atoms with two oxygen neighbors exhibit enhanced electronic hybridization with the O states. Finally, the species observed are reactive to CO oxidation at 200 K and desorption of CO2 leaves a clean and ordered gold surface. (C) 2015 Elsevier B.V. All rights reserved,

The recently discovered (Li0.8Fe0.2)OHFeSe superconductor provides a new platform for exploiting the microscopic mechanisms of high-T-c superconductivity in FeSe-derived systems. Using density functional theory calculations, we first show that substitution of Li by Fe not only significantly strengthens the attraction between the (Li0.8Fe0.2)OH spacing layers and the FeSe superconducting layers along the c axis but also minimizes the lattice mismatch between the two in the ab plane, both favorable for stabilizing the overall structure. Next, we explore the electron injection into FeSe from the spacing layers and unambiguously identify the Fe-0.2 components to be the origin of the dramatically enhanced interlayer charge transfer. We further reveal that the system strongly favors collinear antiferromagnetic ordering in the FeSe layers, but the spacing layers can be either antiferromagnetic or ferromagnetic depending on the Fe-0.2 spatial distribution. Based on these insights, we predict (Li0.8Co0.2)OHFeSe to be structurally stable with even larger electron injection and potentially higher T-c.

The properties of iron-based superconductors (Fe-SCs) can be varied dramatically with the introduction of dopants and atomic defects. As a pressing example, FeSe, parent phase of the highest-T-c Fe-SC, exhibits prevalent defects with atomic-scale ``dumbbell'' signatures as imaged by scanning tunneling microscopy (STM). These defects spoil superconductivity when their concentration exceeds 2.5%. Resolving their chemical identity is a prerequisite to applications such as nanoscale patterning of superconducting/nonsuperconducting regions in FeSe as well as fundamental questions such as the mechanism of superconductivity and the path by which the defects destroy it. We use STM and density functional theory to characterize and identify the dumbbell defects. In contrast to previous speculations about Se adsorbates or substitutions, we find that an Fe-site vacancy is the most energetically favorable defect in Se-rich conditions and reproduces our observed STM signature. Our calculations shed light more generally on the nature of Se capping, the removal of Fe vacancies via annealing, and their ordering into a root 5 x root 5 superstructure in FeSe and related alkali-doped compounds.

We investigate the changes in grain boundary sliding (GBS) and intergranular decohesion in copper (Cu), due to the inclusion of bismuth (Bi), lead (Pb) and silver (Ag) substitutional impurity atoms at a Sigma 5 (012) symmetric tilt grain boundary (GB), using a first-principles concurrent multiscale approach. We first study the segregation behavior of the impurities by determining the impurity segregation energy in the vicinity of the GB. We find that the energetically preferred sites are on the GB plane. We investigate the intergranular decohesion of Cu by Bi and Pb impurities and compare this to the effect of Ag impurities by considering the work of separation, Ws and the tensile strength, st. Both Ws and st decrease in the presence of Bi and Pb impurities, indicating their great propensity for intergranular embrittlement, whilst the presence of Ag impurities has only a small effect. We consider GBS to assess the mechanical properties in nanocrystalline metals and find that all three impurities strongly inhibit GBS, with Ag having the biggest effect. This suggests that Ag has a strong effect on the mechanical properties of nanocrystalline Cu, even though its effect on the intergranular decohesion properties of coarse-grained Cu is not significant.

We investigate the non-equilibrium hydrodynamic effects on the reactivity of a nanoporous catalytic sample. Numerical simulations using the Lattice Boltzmann Method (LBM) show that non-equilibrium effects enhance the reactivity of the porous sample, in agreement with theoretical predictions [1]. In addition, we provide a quantitative assessment of the reactivity in terms of the thickness of the reactive layer inside the nanoporous catalytic sample. Such an assessment constitutes a first step towards integrated simulations encompassing nanoscale reactivity and transport coefficients within a macroscale description of experimental relevance. (C) 2016 Elsevier B.V. All rights reserved.

We use first-principles calculations to investigate the band structure evolution of WX2/MoX2 (X = S, Se) heterobilayers under a perpendicular electric field. We characterize the extent to which the type II band alignment in these compounds can be tuned or inverted electrostatically. Our results demonstrate two effects of the stacking configuration. First, different stackings produce different net dipole moments, resulting in band offset variations that are larger than 0.1 eV. Second, based on symmetry constraints that depend on stacking, a perpendicular electric field may hybridizeWX(2) and MoX2 bands that cross at the Brillouin zone corner K. Our results suggest that external electric fields can be used to tune the physics of intralayer and interlayer excitons in heterobilayers of transition metal dichalcogenides.

We derive electronic structure models for weakly interacting bilayers such as graphene-graphene and graphene-hexagonal boron nitride, based on density functional theory calculations followed by Wannier transformation of electronic states. These transferable interlayer coupling models can be applied to investigate the physics of bilayers with arbitrary translations and twists. The functional form, in addition to the dependence on the distance, includes the angular dependence that results from higher angular momentum components in the Wannier p(z) orbitals. We demonstrate the capabilities of the method by applying it to a rotated graphene bilayer, which produces the analytically predicted renormalization of the Fermi velocity, Van Hove singularities in the density of states, and moire pattern of the electronic localization at small twist angles. We further extend the theory to obtain the effective couplings by integrating out neighboring layers. This approach is instrumental for the design of van der Walls heterostructures with desirable electronic features and transport properties and for the derivation of low-energy theories for graphene stacks, including proximity effects from other layers.

In the hydrogen evolution reaction (HER), the reactivity as a function of the hydrogen adsorption energy on different metal substrates follows a well-known;volcano curve, peaked a the precious inetal Pt. The goal. of turning nonprecious metals into efficient catalysts for HER and other important chemical reactions is a :fundamental challenge; it is-also of technological significance. Here, we present results toward, achieving Ihis goal by exploiting the synergistic power of marginal catalysis and confined catalysis. Using density functional theory calculations, we first show that the volcano curve stays qualitatively intact when van der Waals attractions between a hydrogen adatom and different metal (111) surfaces are included. We further show that the hydrogen adsorption:energy on the metal: surfaces is weakened by 0.11-0.23 eV when hydrogen is confinedbetween graphene and the metal surfaces, with Ni exhibiting the largest change. Inparticular, we find that the graphene-modified volcano curve peaks around Ni, whose bare surface already possesses moderate (or :marginal) reactivity,i and the corresponding HER rate of grapllene-covered comparable to that of bare Pt. A hydrogen adatom has high mobility within the confined geometry. These findings demonstrate that graphene-coyeted Ni is an appealing effective stable, and economical catalytic platform for HER.