VO2: A Novel View from Band Theory

New calculations for vanadium dioxide, one of the most controversely discussed materials for decades, reveal that band theory as based on density functional theory is well capable of correctly describing the electronic and magnetic properties of the metallic as well as both the insulating M1 and M2 phases. Considerable progress in the understanding of the physics of VO2 is achieved by the use of the recently developed hybrid functionals, which include part of the electron-electron interaction exactly and thereby improve on the weaknesses of semilocal exchange functionals as provided by the local density and generalized gradient approximations. Much better agreement with photoemission data as compared to previous calculations is found and a consistent description of the rutile-type early transition-metal dioxides is achieved.Comment: 5 pages, 4 figure

Adsorption of organic molecules at the TiO2(110) surface: the effect of van der Waals interactions

Understanding the interaction of organic molecules with TiO2 surfaces is important for a wide range of technological applications. While density functional theory (DFT) calculations can provide valuable insight about these interactions, traditional DFT approaches with local exchange-correlation functionals suffer from a poor description of non-bonding van der Waals (vdW) interactions. We examine here the contribution of vdW forces to the interaction of small organic molecules (methane, methanol, formic acid and glycine) with the TiO2 (110) surface, based on DFT calculations with the optB88-vdW functional. The adsorption geometries and energies at different configurations were also obtained in the standard generalized gradient approximation (GGA-PBE) for comparison. We find that the optB88-vdW consistently gives shorter surface adsorbate-to-surface distances and slightly stronger interactions than PBE for the weak (physisorbed) modes of adsorption. In the case of strongly adsorbed (chemisorbed) molecules both functionals give similar results for the adsorption geometries, and also similar values of the relative energies between different chemisorption modes for each molecule. In particular both functionals predict that dissociative adsorption is more favourable than molecular adsorption for methanol, formic acid and glycine, in general agreement with experiment. The dissociation energies obtained from both functionals are also very similar, indicating that vdW interactions do not affect the thermodynamics of surface deprotonation. However, the optB88-vdW always predicts stronger adsorption than PBE. The comparison of the methanol adsorption energies with values obtained from a Redhead analysis of temperature programmed desorption data suggests that optB88-vdW significantly overestimates the adsorption strength, although we warn about the uncertainties involved in such comparisons.Comment: 32 pages, 8 figures; accepted in Surface Scienc

Substitutional and orientational disorder in organic crystals: a symmetry-adapted ensemble model

Modelling of disorder in organic crystals is highly desirable since it would allow thermodynamic stabilities and other disorder-sensitive properties to be estimated for such systems. Two disordered organic molecular systems are modeled using a symmetry-adapted ensemble approach, in which the disordered system is treated as an ensemble of the configurations of a supercell with respect to substitution of one disorder component for another. Computation time is kept manageable by performing calculations only on the symmetrically inequivalent configurations. Calculations are presented on a substitutionally disordered system, the dichloro/dibromobenzene solid solution, and on an orientationally disordered system, eniluracil, and the resultant free energies, disorder patterns, and system properties are discussed. The results are found to be in agreement with experiment following manual removal of physically implausible configurations from ensemble averages, highlighting the dangers of a completely automated approach to organic crystal thermodynamics which ignores the barriers to equilibration once the crystal has been formed

The closed-edge structure of graphite and the effect of electrostatic charging

The properties of graphite, and of few-layer graphene, can be strongly influenced by the edge structure of the graphene planes, but there is still much that we do not understand about the geometry and stability of these edges. We present an experimental and theoretical study of the closed edges of graphite crystals, and of the effect of an electric field on their structure. High-resolution transmission electron microscopy is used to image the edge structure of fresh graphite and of graphite that has been exposed to an electric field, which experiences a separation of the graphene layers. Computer simulations based on density functional theory are used to rationalise and quantify the preference for the formation of multiple concentric loops at the edges. A model is also presented to explain how the application of an electric field leads to the separation of the folded edges

Crystal Structure Generation with Autoregressive Large Language Modeling

The generation of plausible crystal structures is often an important step in the computational prediction of crystal structures from composition. Here, we introduce a methodology for crystal structure generation involving autoregressive large language modeling of the Crystallographic Information File (CIF) format. Our model, CrystaLLM, is trained on a comprehensive dataset of millions of CIF files, and is capable of reliably generating correct CIF syntax and plausible crystal structures for many classes of inorganic compounds. Moreover, we provide general and open access to the model by deploying it as a web application, available to anyone over the internet. Our results indicate that the model promises to be a reliable and efficient tool for both crystallography and materials informatics

Predicting Thermoelectric Transport Properties from Composition with Attention-based Deep Learning

Thermoelectric materials can be used to construct devices which recycle waste heat into electricity. However, the best known thermoelectrics are based on rare, expensive or even toxic elements, which limits their widespread adoption. To enable deployment on global scales, new classes of effective thermoelectrics are thus required. $\textit{Ab initio}$ models of transport properties can help in the design of new thermoelectrics, but they are still too computationally expensive to be solely relied upon for high-throughput screening in the vast chemical space of all possible candidates. Here, we use models constructed with modern machine learning techniques to scan very large areas of inorganic materials space for novel thermoelectrics, using composition as an input. We employ an attention-based deep learning model, trained on data derived from $\textit{ab initio}$ calculations, to predict a material's Seebeck coefficient, electrical conductivity, and power factor over a range of temperatures and $\textit{n}$- or $\textit{p}$-type doping levels, with surprisingly good performance given the simplicity of the input, and with significantly lower computational cost. The results of applying the model to a space of known and hypothetical binary and ternary selenides reveal several materials that may represent promising thermoelectrics. Our study establishes a protocol for composition-based prediction of thermoelectric behaviour that can be easily enhanced as more accurate theoretical or experimental databases become available

Double-well potential energy surface in the interaction between h-BN and Ni(111)

Density functional theory calculations with non-local correlation functionals, properly accounting for dispersion forces, predict the presence of two minima in the interaction energy between h-BN and Ni(111). These can be described as a physisorbed state with no corrugation of the h-BN structure, and a chemisorbed state exhibiting noticeable corrugation and shorter distance of h-BN to the metallic support. The latter corresponds indeed to the one reported in most experiments. The relative stability of the two minima depends on the specific density functional employed: of those investigated here only the optB86b-vdW yields the correct order of stability. We also demonstrate that the effect of the metal support on the Raman frequency of the chemisorbed boron nitride monolayer cannot be reduced to the associated strain. This is important because the Raman frequency has been proposed as a signature to identify h-BN monolayers from multilayered samples. Our analysis shows that such signatures would be strongly dependent on the nature of the support – h-BN interaction

Origin of the monolayer Raman signature in hexagonal boron nitride: a first-principles analysis

Monolayers of hexagonal boron nitride (h-BN) can in principle be identified by a Raman signature, consisting of an upshift in the frequency of the E2g vibrational mode with respect to the bulk value, but the origin of this shift (intrinsic or support-induced) is still debated. Herein we use density functional theory calculations to investigate whether there is an intrinsic Raman shift in the h-BN monolayer in comparison with the bulk. There is universal agreement among all tested functionals in predicting the magnitude of the frequency shift upon a variation in the in-plane cell parameter. It is clear that a small in-plane contraction can explain the Raman peak upshift from bulk to monolayer. However, we show that the larger in-plane parameter in the bulk (compared to the monolayer) results from non-local correlation effects, which cannot be accounted for by local functionals or those with empirical dispersion corrections. Using a non-local-correlation functional, we then investigate the effect of finite temperatures on the Raman signature. We demonstrate that bulk h-BN thermally expands in the direction perpendicular to the layers, while the intralayer distances slightly contract, in agreement with observed experimental behavior. Interestingly, the difference in in-plane cell parameter between bulk and monolayer decreases with temperature, and becomes very small at room temperature. We conclude that the different thermal expansion of bulk and monolayer partially "erases" the intrinsic Raman signature, accounting for its small magnitude in recent experiments on suspended samples