Fritz Haber Institute of the Max Planck Society

We present a parameter-free method for an accurate determination of long-range van der Waals interactions from mean-field electronic structure calculations. Our method relies on the summation of interatomic C6 coefficients, derived from the electron density of a molecule or solid and accurate reference data for the free atoms. The mean absolute error in the C6 coefficients is 5.5% when compared to accurate experimental values for 1225 intermolecular pairs, irrespective of the employed exchange-correlation functional. We show that the effective atomic C6 coefficients depend strongly on the bonding environment of an atom in a molecule. Finally, we analyze the van der Waals radii and the damping function in the C6R(-6) correction method for density-functional theory calculations.

Preparation of Solid Catalysts. Characterization of Solid Catalysts. Model Systems. Elementary Steps and Mechanisms. Kinetics and Transport Processes. Deactivation and Regeneration. Special Catalytic Systems. Laboratory Reactors. Reaction Engineering. Environmental Catalysis. Inorganic Reactions. Energy-related Catalysis. Organic Reactions.

Graphitic carbon nitride, g-C3N4, can be made by polymerization of cyanamide, dicyandiamide or melamine. Depending on reaction conditions, different materials with different degrees of condensation, properties and reactivities are obtained. The firstly formed polymeric C3N4 structure, melon, with pendant amino groups, is a highly ordered polymer. Further reaction leads to more condensed and less defective C3N4 species, based on tri-s-triazine (C6N7) units as elementary building blocks. High resolution transmission electron microscopy proves the extended two-dimensional character of the condensation motif. Due to the polymerization-type synthesis from a liquid precursor, a variety of material nanostructures such as nanoparticles or mesoporous powders can be accessed. Those nanostructures also allow fine tuning of properties, the ability for intercalation, as well as the possibility to give surface-rich materials for heterogeneous reactions. Due to the special semiconductor properties of carbon nitrides, they show unexpected catalytic activity for a variety of reactions, such as for the activation of benzene, trimerization reactions, and also the activation of carbon dioxide. Model calculations are presented to explain this unusual case of heterogeneous, metal-free catalysis. Carbon nitride can also act as a heterogeneous reactant, and a new family of metal nitride nanostructures can be accessed from the corresponding oxides.

We describe the synthesis of bilayer graphene thin films deposited on insulating silicon carbide and report the characterization of their electronic band structure using angle-resolved photoemission. By selectively adjusting the carrier concentration in each layer, changes in the Coulomb potential led to control of the gap between valence and conduction bands. This control over the band structure suggests the potential application of bilayer graphene to switching functions in atomic-scale electronic devices.

We present total-energy, force, and electronic-structure calculations for Na and K adsorbed in various geometries on an Al(111) surface. The calculations apply density-functional theory together with the local-density approximation and the ab initio pseudopotential formalism. Two adsorbate meshes, namely, (\ensuremath{\surd}3 \ifmmode\times\else\texttimes\fi{} \ensuremath{\surd}3 )R30\ifmmode^\circ\else\textdegree\fi{} and (2\ifmmode\times\else\texttimes\fi{}2), are considered and for each of them the geometry of the adlayer relative to the substrate is varied over a wide range of possibilities. By total-energy minimization we determine stable and metastable geometries. For Na we find for both adsorbate meshes that the ordering of the calculated binding energies per adatom is such that the substitutional geometry, where each Na atom replaces a surface Al atom, is most favorable and the on-top position is most unfavorable. The (\ensuremath{\surd}3 \ifmmode\times\else\texttimes\fi{} \ensuremath{\surd}3 )R30\ifmmode^\circ\else\textdegree\fi{} structure has a lower energy than the (2\ifmmode\times\else\texttimes\fi{}2) structure. This is shown to be a substrate effect and not an effect of the adsorbate-adsorbate interaction. In contrast to the results for Na, we find for the (\ensuremath{\surd}3 \ifmmode\times\else\texttimes\fi{} \ensuremath{\surd}3 )R30\ifmmode^\circ\else\textdegree\fi{} K adsorption that the calculated adsorption energies for the on-top, threefold hollow, and substitutional sites are equal within the accuracy of our calculation, which is \ifmmode\pm\else\textpm\fi{}0.03 eV. The similarity of the energies of the on-surface adsorption sites is explained as a consequence of the bigger size of K which implies that the adatom experiences a rather small substrate electron-density corrugation. Therefore for potassium the on-top and hollow sites are close in energy already for the unrelaxed Al(111) substrate. Because the relaxation energy of the on-top site is larger than that of the threefold hollow site both sites receive practically the same adsorption energy. The unexpected possibility of surface-substitutional sites is explained as a consequence of the ionic nature of the bonding which, at higher coverages, can develop strongest when the adatom can dive into the substrate as deep as possible. The interesting result of the studied systems is that the difference in bond strengths between the ``normal'' and substitutional geometries is sufficiently large to kick out a surface Al atom.

Mechanisms in Methanol Catalysis The industrial production of methanol from hydrogen and carbon monoxide depends on the use of copper and zinc oxide nanoparticles on alumina oxide supports. This catalyst is “structure sensitive”; its activity can vary by orders of magnitude, depending on how it is prepared. Behrens et al. (p. 893 , published online 19 April; see the Perspective by Greeley ) used a combination of bulk and surface-sensitive analysis and imaging methods—along with insights from density functional theory calculations—to study several catalysts, including the one similar to that used industrially. High activity depended on the presence of steps on the copper nanoparticles stabilized by defects such as stacking faults. Partial coverage of the copper nanoparticles with zinc oxide was critical for stabilizing surface intermediates such as HCO and lowering energetic barriers to the methanol product.

We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a nonlinear statistical regression problem of reduced complexity. Regression models are trained on and compared to atomization energies computed with hybrid density-functional theory. Cross validation over more than seven thousand organic molecules yields a mean absolute error of ∼10 kcal/mol. Applicability is demonstrated for the prediction of molecular atomization potential energy curves.

Zinc oxide, a wide-band-gap semiconductor with many technological applications, typically exhibits n-type conductivity. The cause of this conductivity has been widely debated. A first-principles investigation, based on density functional theory, produces strong evidence that hydrogen acts as a source of conductivity: it can incorporate in high concentrations and behaves as a shallow donor. This behavior is unexpected and very different from hydrogen's role in other semiconductors, in which it acts only as a compensating center and always counteracts the prevailing conductivity. These insights have important consequences for control and utilization of hydrogen in oxides in general.

Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20-fullerene that would have been infeasible with regular ab initio molecular dynamics.

Using density-functional theory we calculate the Gibbs free energy to determine the lowest-energy structure of a ${\mathrm{RuO}}_{2}(110)$ surface in thermodynamic equilibrium with an oxygen-rich environment. The traditionally assumed stoichiometric termination is only found to be favorable at low oxygen chemical potentials, i.e., low pressures and/or high temperatures. At a realistic O pressure, the surface is predicted to contain additional terminal O atoms. Although this O excess defines a so-called polar surface, we show that the prevalent ionic model, that dismisses such terminations on electrostatic grounds, is of little validity for ${\mathrm{RuO}}_{2}(110).$ Together with analogous results obtained previously at the (0001) surface of corundum-structured oxides, these findings on (110) rutile indicate that the stability of nonstoichiometric terminations is a more general phenomenon of transition metal oxide surfaces.

Abstract Propelled partly by the Materials Genome Initiative, and partly by the algorithmic developments and the resounding successes of data-driven efforts in other domains, informatics strategies are beginning to take shape within materials science. These approaches lead to surrogate machine learning models that enable rapid predictions based purely on past data rather than by direct experimentation or by computations/simulations in which fundamental equations are explicitly solved. Data-centric informatics methods are becoming useful to determine material properties that are hard to measure or compute using traditional methods—due to the cost, time or effort involved—but for which reliable data either already exists or can be generated for at least a subset of the critical cases. Predictions are typically interpolative, involving fingerprinting a material numerically first, and then following a mapping (established via a learning algorithm) between the fingerprint and the property of interest. Fingerprints, also referred to as “descriptors”, may be of many types and scales, as dictated by the application domain and needs. Predictions may also be extrapolative—extending into new materials spaces—provided prediction uncertainties are properly taken into account. This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices. The review also identifies some challenges the community is facing and those that should be overcome in the near future.

) ranked by their probability of being stable as perovskite. This work guides experimentalists and theorists toward which perovskites are most likely to be successfully synthesized and demonstrates an approach to descriptor identification that can be extended to arbitrary applications beyond perovskite stability predictions.

The widespread popularity of density functional theory has given rise to an extensive range of dedicated codes for predicting molecular and crystalline properties. However, each code implements the formalism in a different way, raising questions about the reproducibility of such predictions. We report the results of a community-wide effort that compared 15 solid-state codes, using 40 different potentials or basis set types, to assess the quality of the Perdew-Burke-Ernzerhof equations of state for 71 elemental crystals. We conclude that predictions from recent codes and pseudopotentials agree very well, with pairwise differences that are comparable to those between different high-precision experiments. Older methods, however, have less precise agreement. Our benchmark provides a framework for users and developers to document the precision of new applications and methodological improvements.

We have grown an atom-thin, ordered, two-dimensional multi-phase film in situ through germanium molecular beam epitaxy using a gold (111) surface as a substrate. Its growth is similar to the formation of silicene layers on silver (111) templates. One of the phases, forming large domains, as observed in scanning tunneling microscopy, shows a clear, nearly flat, honeycomb structure. Thanks to thorough synchrotron radiation core-level spectroscopy measurements and advanced density functional theory calculations we can identify it as a √3 × √3 R(30°) germanene layer in conjunction with a √7 × √7 R(19.1°) Au(111) supercell, presenting compelling evidence of the synthesis of the germanium-based cousin of graphene on gold.

An efficient method is developed for the microscopic description of the frequency-dependent polarizability of finite-gap molecules and solids. This is achieved by combining the Tkatchenko-Scheffler van der Waals (vdW) method [Phys. Rev. Lett. 102, 073005 (2009)] with the self-consistent screening equation of classical electrodynamics. This leads to a seamless description of polarization and depolarization for the polarizability tensor of molecules and solids. The screened long-range many-body vdW energy is obtained from the solution of the Schrödinger equation for a system of coupled oscillators. We show that the screening and the many-body vdW energy play a significant role even for rather small molecules, becoming crucial for an accurate treatment of conformational energies for biomolecules and binding of molecular crystals. The computational cost of the developed theory is negligible compared to the underlying electronic structure calculation.

Abstract Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol −1 ) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

The spatio–temporal formation of patterns on the surface during a chemical reaction is one phenomenon that can now be understood and modeled thanks to the Nobel Prize winning research on the course of heterogeneous catalysis. The picture shows a pattern formed by a feedback mechanism during the oxidation of CO. Reactions that have been illuminated by this work include the synthesis of ammonia and the purification of waste gases. Supporting information for this article is available on the WWW under http://www.wiley-vch.de/contents/jc_2002/2008/a800480_s.html or from the author. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

High-resolution scanning tunneling microscopy data on the reconstructed Au(111) surface are presented that give a comprehensive picture of the atomic structure, the long-range ordering, and the interaction between reconstruction and surface defects in the reconstructed surface. On the basis of the atomically resolved structure, the stacking-fault-domain model involving periodic transitions from fcc to hcp stacking of top-layer atoms is confirmed. The practically uniform contraction in the surface layer along [11\ifmmode\bar\else\textasciimacron\fi{}0] indicates that the previously proposed soliton functionalisms are not correct descriptions for the fcc\ensuremath{\rightarrow}hcp stacking transition. The lateral displacement of \ensuremath{\sim}0.9 \AA{} in the ${(}_{\mathrm{\ensuremath{-}}1}^{22}$ $_{2}^{0}$) unit cell along [112\ifmmode\bar\else\textasciimacron\fi{}] is in good agreement with the transition between fcc and hcp stacking. The vertical displacement in the transition regions (0.20\ifmmode\pm\else\textpm\fi{}0.05 \AA{}) is largely independent of the tunneling parameters, while the atomic corrugation (0.2 \AA{} typically, up to 1 \AA{}) depends strongly on tunneling parameters and tip conditions.The two different stacking regions within the unit cell are directly identified from the domain pattern at step edges; fcc stacking is deduced for the wider areas and thus is energetically more favorable. A new long-range superstructure is reported. It is created by a correlated periodic bending of the parallel corrugation lines by \ifmmode\pm\else\textpm\fi{}120\ifmmode^\circ\else\textdegree\fi{} every 250 \AA{}, i.e., rotational domains are arranged in a zigzag pattern. Interactions on this scale indicate long-range elastic lattice strain. This structure reflects the overall tendency to isotropic contraction, combining the locally favorable uniaxial contraction and an effective isotropic contraction on a larger scale. Boundaries of rotational domains can also be formed by a termination of the reconstruction lines. Individual corrugation lines, separating different stacking regions, cannot disappear. The termination occurs in well-ordered, U-shaped connections of neighbored lines or by a complicated pattern of entangled corrugation lines. Steps and bulk defects do not inhibit the reconstruction, but can affect the local reconstruction pattern. In most cases steps are crossed by the reconstruction lines, and the strict correlation of the reconstruction pattern on the terraces, both in phase and orientation, reflects interaction over the step edge. Sometimes the reconstruction pattern at the steps resembles those found at rotational domain boundaries.

1. Introduction 2. Synthesis of Novel Nanostructured Carbon Materials 2.1. Carbon Nanotubes 2.2. Graphene 2.3. Ordered Mesoporous Carbon 2.4. Carbon Hierarchy 2.5. Macroscopic Shaping of Nanocarbon 3. Functionalization of Nanocarbon Materials 3.1. Liquid-Phase Functionalization of Nanocarbon 3.2. Gas-Phase Functionalization of Nanocarbon 4. Characterization and Modeling 4.1. Characterization of Carbon 4.1.1. Microcalorimetry 4.1.2. Temperature-Programmed and Ambient Pressure Photoelectron Spectroscopy 4.1.3. Advanced Electron Microscopy 4.2. Theoretical Modeling 5. Nanocarbons in Catalytic Reactions 5.1. Enhanced Characteristics as a Support for Catalytic Functionalities 5.2. Stabilization of Small Catalytic Particles with Enhanced Catalytic Behavior 5.3. Direct Catalytic Role of Nanocarbon Functional Groups 5.4. Nanoconfinement 5.5. Electron-Transfer Induced Changes in the Properties of Supported Nanoparticles 5.6. Defect-Related Catalytic Reactivity 5.7. Catalysis by Two-Dimensional Carbon Nanomaterials 6. Concluding Remarks and Perspectives

Herein we report the synthesis of a crystalline graphitic carbon nitride, or g-C(3)N(4), obtained from the temperature-induced condensation of dicyandiamide (NH(2)C(=NH)NHCN) by using a salt melt of lithium chloride and potassium chloride as the solvent. The proposed crystal structure of this g-C(3)N(4) species is based on sheets of hexagonally arranged s-heptazine (C(6)N(7)) units that are held together by covalent bonds between C and N atoms which are stacked in a graphitic, staggered fashion, as corroborated by powder X-ray diffractometry and high-resolution transmission electron microscopy.