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Compound materials, such as transition-metal (TM) carbides, are anticipated to be effective electrocatalysts for the carbon dioxide reduction reaction (CO2RR) to useful chemicals. This expectation is nurtured by density functional theory (DFT) predictions of a break of key adsorption energy scaling relations that limit CO2RR at parent TMs. Here, we evaluate these prospects for hexagonal Mo2C in aqueous electrolytes in a multimethod experiment and theory approach. We find that surface oxide formation completely suppresses the CO2 activation. The oxides are stable down to potentials as low as −1.9 V versus the standard hydrogen electrode, and solely the hydrogen evolution reaction (HER) is found to be active. This generally points to the absolute imperative of recognizing the true interface establishing under operando conditions in computational screening of catalyst materials. When protected from ambient air and used in nonaqueous electrolyte, Mo2C indeed shows CO2RR activity.

Cyclic voltammograms (CVs) are a central experimental tool for assessing the structure and activity of electrochemical interfaces. Based on a mean-field ansatz for the interface energetics under applied potential conditions, we here derive an ab initio thermodynamics approach to efficiently simulate thermodynamic CVs. All unknown parameters are determined from density functional theory (DFT) calculations coupled to an implicit solvent model. For the showcased CVs of Ag(111) electrodes in halide-anion-containing solutions, these simulations demonstrate the relevance of double-layer contributions to explain experimentally observed differences in peak shapes over the halide series. Only the appropriate account of interfacial charging allows us to capture the differences in equilibrium coverage and total electronic surface charge that cause the varying peak shapes. As a case in point, this analysis demonstrates that prominent features in CVs do not only derive from changes in adsorbate structure or coverage but can also be related to variations of the electrosorption valency. Such double-layer effects are proportional to adsorbate-induced changes in the work function and/or interfacial capacitance. They are thus especially pronounced for electronegative halides and other adsorbates that affect these interface properties. In addition, the analysis allows us to draw conclusions on how the possible inaccuracy of implicit solvation models can indirectly affect the accuracy of other predicted quantities such as CVs.

The combination of modern machine learning (ML) approaches with high-quality data from quantum mechanical (QM) calculations can yield models with an unrivalled accuracy/cost ratio. However, such methods are ultimately limited by the computational effort required to produce the reference data. In particular, reference calculations for periodic systems with many atoms can become prohibitively expensive for higher levels of theory. This trade-off is critical in the context of organic crystal structure prediction (CSP). Here, a data-efficient ML approach would be highly desirable, since screening a huge space of possible polymorphs in a narrow energy range requires the assessment of a large number of trial structures with high accuracy. In this contribution, we present tailored Δ-ML models that allow screening a wide range of crystal candidates while adequately describing the subtle interplay between intermolecular interactions such as H-bonding and many-body dispersion effects. This is achieved by enhancing a physics-based description of long-range interactions at the density functional tight binding (DFTB) level—for which an efficient implementation is available—with a short-range ML model trained on high-quality first-principles reference data. The presented workflow is broadly applicable to different molecular materials, without the need for a single periodic calculation at the reference level of theory. We show that this even allows the use of wavefunction methods in CSP., Using a cluster-based training scheme and a physical baseline, data efficient machine-learning models for crystal structure prediction are developed, enabling accurate structural relaxations of molecular crystals with unprecedented efficiency.

Computational screening of metal oxide catalysts is challenging due to their more localized and intricate electronic structure as compared to metal catalysts and the resulting lack of suitable acti...

Computational screening based on first-principles microkinetic modeling has evolved into a widespread tool for catalyst discovery. Efficiently exploiting various scaling relations, this approach dr...

The surfaces of compound catalyst materials generally exhibit a wide range of geometric and compositional motives that could act as active sites. We address this inherent complexity by extending co...

The Density-Functional Tight Binding (DFTB) method is a popular semiempirical approximation to Density Functional Theory (DFT). In many cases, DFTB can provide comparable accuracy to DFT at a fraction of the cost, enabling simulations on length and time scales that are unfeasible with first-principles DFT. At the same time (and in contrast to empirical interatomic potentials and force fields), DFTB still offers direct access to electronic properties such as the band structure. These advantages come at the cost of introducing empirical parameters to the method, leading to a reduced transferability compared to true first-principle approaches. Consequently, it would be very useful if the parameter sets could be routinely adjusted for a given project. While fairly robust and transferable parametrization workflows exist for the electronic structure part of DFTB, the so-called repulsive potential Vrep poses a major challenge. In this paper, we propose a machine-learning (ML) approach to fitting Vrep, using Gaussian Process Regression (GPR) to reconstruct Vrep with DFT-DFTB force residues as training data. The use of GPR circumvents the need for nonlinear or global parameter optimization, while at the same time offering arbitrary flexibility in terms of the functional form. We also show that the proposed method can be applied to multiple elements at once, by fitting repulsive potentials for organic molecules containing carbon, hydrogen, and oxygen. Overall, the new approach removes focus from the choice of functional form and parametrization procedure, in favor of a data-driven philosophy.

The breakdown of macrocyclic compounds is of utmost importance in manifold biological and chemical processes, usually proceeding via oxygenation-induced ring-opening reactions. Here, we introduce a surface chemical route to selectively break a prototypical porphyrin species, cleaving off one pyrrole unit and affording a tripyrrin derivative. This pathway, operational in an ultrahigh vacuum environment at moderate temperature is enabled by a distinct molecular conformation achieved via the specific interaction between the porphyrin and its copper support. We provide an atomic-level characterization of the surface-anchored tripyrrin, its reaction intermediates, and byproducts by bond-resolved atomic force microscopy, unequivocally identifying the molecular skeletons. The ring-opening is rationalized by the distortion reducing the macrocycle's stability. Our findings open a route to steer ring-opening reactions by conformational design and to study intriguing tetrapyrrole catabolite analogues on surfaces.

The deeper reduction of CO to multi-carbon-based fuels with higher energy densities and wider applicability has recently triggered extensive experimental and theoretical research. However, present-day catalysts for the generation of multi-carbon products (C2+) suffer from ultra-high energy barrier for C-C bond formation and poor selectivity, which poses great challenges for practical application. Herein, we propose and evidence with first-principles calculations a novel catalyst for the conversion of CO into more value-added ethylene and ethanol under visible light, consisting of active Cu-B atomic pair decorated graphitic carbon nitride (Cu-B@g-C3N4). Our results show that the low coordinated Cu-B atomic pair anchored on g-C3N4 could effectively reduce the CO dimerization free energy barrier to a record value of 0.63 eV. This high catalytic performance stems from the moderate binding strength of the intermediates modulated by the asymmetric synergy of the atomically dispersed metal Cu and non-metal B that can break the linear scaling relationship of traditional transitional metal catalysts. Moreover, the low-coordinated Cu-B active site with synergistic d-p coupling can significantly suppress the parasitic hydrogen evolution reaction. Compared to pure g-C3N4, the Cu-B@g-C3N4 catalyst also becomes more optically active under visible and even infrared light. Ab initio molecular dynamics simulations suggest that the Cu-B@g-C3N4 catalyst possesses a high thermal stability. Considering that efficient catalysts for C2+ production are currently essentially limited to Cu-bimetallic systems, our work highlights a fully new concept towards the development of novel CO reduction catalysts based on synergistic coupling between metal Cu and non-metal atoms.

At the cell voltages required to reach technologically viable current densities in proton-exchange membrane (PEM) electrolyzers, IrO2 catalysts are suspected to undergo a transformation to an amorphous hydrous form. Here, we present a systematic ab initio thermodynamics study analyzing the shape and stability of IrO2 nanoparticles in this potential range. Our results confirm a thermodynamic instability of the rutile crystal structure induced by the stabilization of highly oxidized O species at the surface already at onset potentials for the oxygen evolution reaction (OER). Intriguingly, this is preceded by a transformation of the equilibrium shape at even lower potentials. Instead of the well-studied IrO2(110) facets, this shape is dominated by IrO2(111) facets that have hitherto barely received attention. Our findings highlight the need to extend detailed characterization studies to this high-potential range, not the least to establish more suitable active-site models for the OER that may then serve as t...

Antiperovskite glasses such as Li3OCl and doped analogues have been proposed as excellent electrolytes for all-solid-state Li ion batteries (ASSB). Incorporating these electrolytes in ASSBs results in puzzling properties. This Letter describes a theoretical Li3OCl glass created by conventional melt-quench procedures. The ion conductivities are calculated using molecular dynamics based on a polarizable force field that is fitted to an extensive set of density functional theory-based energies, forces, and stresses for a wide range of nonequilibrium structures encompassing crystal, glass, and melt. We find high Li+ ion conductivity in good agreement with experiments. However, we also find that the Cl- ion is mobile as well so that the Li3OCl glass is not a single-ion conductor, with a transference number t + ≈ 0.84. This has important implications for its use as an electrolyte for all-solid-state batteries because the Cl could react irreversibly with the electrodes and/or produce glass decomposition during discharge-charge.

We implemented the popular Hubbard density-functional theory + U (DFT+U) method in its spherically averaged form in the all-electron, full-potential DFT code FHI-aims. There, electronic states are expressed on a basis of highly localized numeric atomic orbitals (NAO), which straightforwardly lend themselves as projector functions for the DFT+U correction, yielding the necessary occupations of the correlated Hubbard subspace at no additional cost. We establish the efficacy of our implementation on the prototypical bulk NiO and obtain the well-known band gap opening effect of DFT+U. As a more stringent, real world test system, we then study polaron formation at the rutile TiO2(110) surface, where our results are in line with both experimental data as well as hybrid functional calculations. At this TiO2 test system, yet in the bulk, we analyze some of the intricacies of using the DFT+U correction in a localized, numeric atomic orbital basis set. Specifically, we find that multiple localized radial basis func...

Based on a mean-field description of thermodynamic cyclic voltammograms (CVs), we analyze here in full generality, how CV peak positions and shapes are related to the underlying interface energetics, in particular when also including electrostatic double layer (DL) effects. We show in particular, how non-Nernstian behaviour is related to capacitive DL charging, and how this relates to common adsorbate-centered interpretations such as a changed adsorption energetics due to dipole-field interactions and the electrosorption valency – the number of exchanged electrons upon electrosorption per adsorbate. Using Ag(111) in halide-containing solutions as test case, we demonstrate that DL effects can introduce peak shifts that are already explained by rationalizing the interaction of isolated adsorbates with the interfacial fields, while alterations of the peak shape are mainly driven by the coverage-dependence of the adsorbate dipoles. In addition, we analyze in detail how changing the experimental conditions such as the ion concentrations in the solvent but also of the background electrolyte can affect the CV peaks via their impact on the potential drop in the DL and the DL capacitance, respectively. These results suggest new routes to analyze experimental CVs and use of those for a detailed assessment of the accuracy of atomistic models of electrified interfaces e.g. with and without explicitly treated interfacial solvent and/or approximate implicit solvent models.

ACS nano 15(6), 9638 - 9648 (2021). doi:10.1021/acsnano.0c10377, The synthesis of large, defect-free two-dimensional materials (2DMs) such as graphene is a major challenge toward industrial applications. Chemical vapor deposition (CVD) on liquid metal catalysts (LMCats) is a recently developed process for the fast synthesis of high-quality single crystals of 2DMs. However, up to now, the lack of in situ techniques enabling direct feedback on the growth has limited our understanding of the process dynamics and primarily led to empirical growth recipes. Thus, an in situ multiscale monitoring of the 2DMs structure, coupled with a real-time control of the growth parameters, is necessary for efficient synthesis. Here we report real-time monitoring of graphene growth on liquid copper (at 1370 K under atmospheric pressure CVD conditions) via four complementary in situ methods: synchrotron X-ray diffraction and reflectivity, Raman spectroscopy, and radiation-mode optical microscopy. This has allowed us to control graphene growth parameters such as shape, dispersion, and the hexagonal supra-organization with very high accuracy. Furthermore, the switch from continuous polycrystalline film to the growth of millimeter-sized defect-free single crystals could also be accomplished. The presented results have far-reaching consequences for studying and tailoring 2D material formation processes on LMCats under CVD growth conditions. Finally, the experimental observations are supported by multiscale modeling that has thrown light into the underlying mechanisms of graphene growth., Published by Soc., Washington, DC

ConspectusHeterogeneous catalysts are rather complex materials that come in many classes (e.g., metals, oxides, carbides) and shapes. At the same time, the interaction of the catalyst surface with even a relatively simple gas-phase environment such as syngas (CO and H2) may already produce a wide variety of reaction intermediates ranging from atoms to complex molecules. The starting point for creating predictive maps of, e.g., surface coverages or chemical activities of potential catalyst materials is the reliable prediction of adsorption enthalpies of all of these intermediates. For simple systems, direct density functional theory (DFT) calculations are currently the method of choice. However, a wider exploration of complex materials and reaction networks generally requires enthalpy predictions at lower computational cost.The use of machine learning (ML) and related techniques to make accurate and low-cost predictions of quantum-mechanical calculations has gained increasing attention lately. The employed approaches span from physically motivated models over hybrid physics-ΔML approaches to complete black-box methods such as deep neural networks. In recent works we have explored the possibilities for using a compressed sensing method (Sure Independence Screening and Sparsifying Operator, SISSO) to identify sparse (low-dimensional) descriptors for the prediction of adsorption enthalpies at various active-site motifs of metals and oxides. We start from a set of physically motivated primary features such as atomic acid/base properties, coordination numbers, or band moments and let the data and the compressed sensing method find the best algebraic combination of these features. Here we take this work as a starting point to categorize and compare recent ML-based approaches with a particular focus on model sparsity, data efficiency, and the level of physical insight that one can obtain from the model.Looking ahead, while many works to date have focused only on the mere prediction of databases of, e.g., adsorption enthalpies, there is also an emerging interest in our field to start using ML predictions to answer fundamental science questions about the functioning of heterogeneous catalysts or perhaps even to design better catalysts than we know today. This task is significantly simplified in works that make use of scaling-relation-based models (volcano curves), where the model outcome is determined by only one or two adsorption enthalpies and which consequently become the sole target for ML-based high-throughput screening or design. However, the availability of cheap ML energetics also allows going beyond scaling relations. On the basis of our own work in this direction, we will discuss the additional physical insight that can be achieved by integrating ML-based predictions with traditional catalysis modeling techniques from thermal and electrocatalysis, such as the computational hydrogen electrode and microkinetic modeling, as well as the challenges that lie ahead.

The versatility of organic molecules generates a rich design space for organic semiconductors (OSCs) considered for electronics applications. Offering unparalleled promise for materials discovery, the vastness of this design space also dictates efficient search strategies. Here, we present an active machine learning (AML) approach that explores an unlimited search space through consecutive application of molecular morphing operations. Evaluating the suitability of OSC candidates on the basis of charge injection and mobility descriptors, the approach successively queries predictive-quality first-principles calculations to build a refining surrogate model. The AML approach is optimized in a truncated test space, providing deep methodological insight by visualizing it as a chemical space network. Significantly outperforming a conventional computational funnel, the optimized AML approach rapidly identifies well-known and hitherto unknown molecular OSC candidates with superior charge conduction properties. Most importantly, it constantly finds further candidates with highest efficiency while continuing its exploration of the endless design space., Existing methods for organic semiconductor computational screening are limited by the computational demand of the process, leading to the identification of non-optimal material candidates. Here, the authors report machine learning method to guide the discovery of organic semiconductors.

Improving charge carrier mobilities in organic semiconductors is a challenging task that has hitherto primarily been tackled by empirical structural tuning of promising core compounds. Knowledge-based methods can greatly accelerate such local exploration, while a systematic analysis of large chemical databases can point towards promising design strategies. Here, we demonstrate such data mining by clustering an in-house database of >64.000 organic molecular crystals for which two charge-transport descriptors, the electronic coupling and the reorganization energy, have been calculated from first principles. The clustering is performed according to the Bemis-Murcko scaffolds of the constituting molecules and according to the sidegroups with which these molecular backbones are functionalized. In both cases, we obtain statistically significant structure-property relationships with certain scaffolds (sidegroups) consistently leading to favorable charge-transport properties. Functionalizing promising scaffolds with favorable sidegroups results in engineered molecular crystals for which we indeed compute improved charge-transport properties.

Implicit solvation is an effective, highly coarse-grained approach in atomic-scale simulations to account for a surrounding liquid electrolyte on the level of a continuous polarizable medium. Originating in molecular chemistry with finite solutes, implicit solvation techniques are now increasingly used in the context of first-principles modeling of electrochemistry and electrocatalysis at extended (often metallic) electrodes. The prevalent ansatz to model the latter electrodes and the reactive surface chemistry at them through slabs in periodic boundary condition supercells brings its specific challenges. Foremost this concerns the diffculty to describe the entire double layer forming at the electrified solid-liquid interface (SLI) within supercell sizes tractable by commonly employed density-functional theory (DFT). We review liquid solvation methodology from this specific application angle, highlighting in particular its use in the widespread {\em ab initio} thermodynamics approach to surface catalysis. Notably, implicit solvation can be employed to mimic a polarization of the electrode's electronic density under the applied potential and the concomitant capacitive charging of the entire double layer beyond the limitations of the employed DFT supercell. Most critical for continuing advances of this effective methodology for the SLI context is the lack of pertinent (experimental or high-level theoretical) reference data needed for parametrization.

Supercages of faujasite (FAU)‐type zeolites serve as a robust scaffold for stabilizing dinuclear (Mo2S4) and tetranuclear (Mo4S4) molybdenum sulfide clusters. The FAU‐encaged Mo4S4 clusters have a distorted cubane structure similar to the FeMo‐cofactor in nitrogenase. Both clusters possess unpaired electrons on Mo atoms. Additionally, they show identical catalytic activity per sulfide cluster. Their catalytic activity is stable (>150 h) for ethene hydrogenation, while layered MoS2 structures deactivate significantly under the same reaction conditions., Dinuclear (Mo2S4) and tetranuclear (Mo4S4) molybdenum sulfide clusters were synthesized within the supercages of a faujasite zeolite. Encaged Mo4S4 clusters possess a structure similar to the nitrogenase enzyme's FeMo‐cofactor; both contain unpaired electrons and show identical catalytic activity per cluster. Their activity for ethene hydrogenation is stable, while layered MoS2 deactivates significantly under the same conditions.

Controllable large-scale synthesis of two-dimensional materials (2DMs) such as graphene is a prerequisite for industrial applications. Chemical vapor deposition (CVD) is currently the most widespread synthesis method as it is efficient and easy to automatize. The process itself is quite complex and poorly understood, but it is generally believed to involve a number of distinct steps such as hydrocarbon decomposition into surface-bound intermediates, diffusion on the catalytic substrate, generation of nucleation points and, finally, graphene growth. In situ monitoring and tailoring of such a complex procedure is beneficial for understanding the growth kinetics and, eventually, for controlling the graphene growth. Herein, we report on a novel metrology system based on in situ reflectance spectroscopy that has been developed for real-time monitoring of surface changes during graphene growth on Cu foils at high operating temperatures. The implementation of this technique for extracting kinetic parameters of the growth process is presented. Furthermore, a microkinetic model of graphene growth based on density-functional theory (DFT) and the hindered translator / rotator model for enthalpy and entropy corrections is constructed and used to obtain a microscopic understanding of the apparent activation energy and related rate-determining steps in graphene growth.

A Gaussian approximation potential was trained using density-functional theory data to enable a global geometry optimization of low-index rutile IrO_{2} facets through simulated annealing. Ab initio thermodynamics identifies (101) and (111) (1×1) terminations competitive with (110) in reducing environments. Experiments on single crystals find that (101) facets dominate and exhibit the theoretically predicted (1×1) periodicity and x-ray photoelectron spectroscopy core-level shifts. The obtained structures are analogous to the complexions discussed in the context of ceramic battery materials.

Conspectus The visualization of data is indispensable in scientific research, from the early stages when human insight forms to the final step of communicating results. In computational physics, chemistry and materials science, it can be as simple as making a scatter plot or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the “big data” revolution, these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bioactivities. It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural data sets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them. This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large data sets but also reveal hidden patterns that could be easily missed using conventional analyses. The explosion in the amount of computed information in chemistry and materials science has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of data sets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields.

A new class of macrocyclic angle-strained alkynes whose size and reactivity can be precisely tuned by modular organic synthesis is disclosed. Detailed analysis of the size-dependent structural and electronic properties provides evidence for considerable distortion of the alkyne units incorporated into the cycloparaphenylene (CPP)-derived macrocycles. The remarkable increase of the alkyne reactivity with decreasing macrocycle size in [2+2]cycloaddition-retrocyclization was investigated by joint experimental and theoretical studies and the thermodynamic and kinetic parameters that govern this reaction were unraveled. Additionally, even the largest, least strained macrocycle in this series was found to undergo strain-promoted azide-alkyne cycloaddition (SPAAC) efficiently under mild conditions, thereby paving the way to the application of alkyne-containing CPPs as fluorescent "clickable" macrocyclic architectures.

Density functional theory was used to examine the oxygen evolution reaction on a large number of active sites formed by doping three different surfaces of Co3O4 with various 3d transition-metal atoms. By combining the scaling and Bronsted–Evans–Polanyi (BEP) relationships that are determined for these sites, it is shown that the activity of a site is controlled by the redox potential for oxidation of the site. On the basis of this, a kinetics-based design strategy is presented for identifying the optimal active site at a given electrode potential. This design strategy is shown to be valid regardless of whether the rate-limiting water addition step occurs electrochemically or nonelectrochemically, as long as certain conditions are met. Another finding is that the BEP relations are sensitive to the structure of the active site, with sites reacting through μ3-oxo species having the most favorable relations. Finally, the kinetics-based design strategy is compared with the commonly used thermodynamics-based de...

We describe the formation of lanthanide-organic coordination networks and complexes under ultra-high-vacuum conditions on a clean Ag(100) surface. The structures comprise single Ho atoms as coordination centers and 1,4-benzenedicarboxylate (from terephtalic acid, TPA) as molecular linkers. Using low temperature scanning tunneling microscopy, we find two different chiral phases of surface-supported metal-organic structures incorporating Ho atoms. Density functional theory calculations can explain the structure of both binding motifs and give possible reasons for their varying formation under the respective Ho/TPA ratios, as well as deposition and annealing temperatures. Metal-ligand interactions drive the formation of cloverleaf-shaped mononuclear Ho-TPA(4) complexes establishing supramolecular arrays stabilized through hydrogen bonding. A 2D lanthanide-organic reticulation is observed when changing the stoichiometry between the two building blocks. The combined insights from scanning tunneling microscopy and density functional theory reveal the relative stability, charge transfer, and bonding environment of both motifs.

We present a novel approach for calculating the static dielectric permittivity profile of a liquid–liquid interface (LLI) from molecular dynamics simulations. To obtain well-defined features, comparable to those observed at solid–liquid interfaces, we find it essential to reference to the instantaneous liquid–liquid interface rather than the more commonly used average Gibbs interface. We provide a coarse-grained approach for the practical definition of the instantaneous interface and present numerical results for the prototypical water/1,2-dichloroethane system. These results show that the parallel components of the dielectric permittivity tensor can be accurately extracted. In contrast, the perpendicular component does not converge to the correct bulk value at large distances from the LLI, highlighting a flaw in the regularly applied coarse-graining procedure.

Calculating the electronic structure of molecules and solids has become an important pillar of modern research in diverse fields of research from biology and materials science to chemistry and physics. Unfortunately, increasingly accurate and thus reliable approximate solution schemes to the underlying Schrödinger equation scale steeply in computational cost, rendering most accurate approaches like "gold standard" coupled cluster theory, CC, quickly intractable for larger systems of interest. Here we show that this scaling can be significantly reduced by applying machine-learning to the CC correlation energy. We introduce a vector-based representation of CC wave functions and use potential energy surfaces of a small molecule test set to learn the correlation energy from this representation. Our results show that the CC correlation energy can be efficiently learned, even when the representation is constructed from approximate amplitudes provided by computationally less demanding Møller-Plesset (MP2) perturbation theory. Exploiting existing linear scaling MP2 implementations, this potentially opens the door to CC-quality molecular dynamics simulations.

Despite their industrial use in selective hydrogenation reactions, the surface chemistry of long-chained alkynes on transition metals is not well understood. To this end, the two C6-alkynes 1- and 3-hexyne were studied on Pt(111) using temperature-programmed desorption (TPD), electron emission spectroscopies (MIES/UPS), and infrared reflection–absorption spectroscopy (IRRAS). Besides the formation of graphitic carbon residues, both molecules mainly undergo desorption, self-hydrogenation, and dehydrocyclization to form benzene during temperature-programmed desorption, similar to the analogous alkenes. The dehydrocyclization to benzene is shown to be ubiquitous to unsaturated hydrocarbons on Pt(111) regardless of the degree of unsaturation and its position within the molecule. A reaction mechanism for dehydrocyclization is proposed based on dehydrogenation followed by ring-closure. This work extends the understanding of alkyne chemistry on Pt-based catalysts and may aid to identify additional reaction mecha...

An accurate atomistic treatment of aqueous solid–liquid interfaces necessitates the explicit description of interfacial water ideally via ab initio molecular dynamics simulations. Many applications, however, still rely on static interfacial water models, e.g., for the computation of (electro)chemical reaction barriers and focus on a single, prototypical structure. In this work, we systematically study the relation between density functional theory-derived static and dynamic interfacial water models with specific focus on the water–Pt(111) interface. We first introduce a general construction protocol for static 2D water layers on any substrate, which we apply to the low index surfaces of Pt. Subsequently, we compare these with structures from a broad selection of reference works based on the Smooth Overlap of Atomic Positions descriptor. The analysis reveals some structural overlap between static and dynamic water ensembles; however, static structures tend to overemphasize the in-plane hydrogen bonding network. This feature is especially pronounced for the widely used low-temperature hexagonal ice-like structure. In addition, a complex relation between structure, work function, and adsorption energy is observed, which suggests that the concentration on single, static water models might introduce systematic biases that are likely reduced by averaging over consistently created structural ensembles, as introduced here.

Even though the surface-assisted dehalogenative coupling constitutes the most abundant protocol in on-surface synthesis, its full potential will only become visible if selectivity issues with polybrominated precursors are comprehensively understood, opening new venues for both organometallic self-assembly and on-surface polymerization. Using the 3,3',5,5'-tetrabromo-2,2',4,4',6,6'-hexafluorobiphenyl (Br4F6BP) at Ag(111), we demonstrate a remote site-selective functionalization at room temperature and a marked temperature difference in double- vs. quadruple activation, both phenomena caused by conformational mechanical effects of the precursor-surface ensemble. The submolecularly resolved structural characterization was achieved by Scanning Tunneling Microscopy, the chemical state was quantitatively assessed by X-ray Photoelectron Spectroscopy, and the analysis of the experimental signatures was supported through first-principles Density-Functional Theory calculations. The non-planarity of the various structures at the surface was specifically probed by additional Near Edge X-ray Absorption Fine Structure experiments. Upon progressive heating, Br4F6BP on Ag(111) shows the following unprecedented phenomena: (1) formation of regular organometallic 1D chains via remote site-selective 3,5'-didebromination; (2) a marked temperature difference in double- vs. quadruple activation; (3) an organometallic self-assembly based on reversibility of C-Ag-C linkages with a thus far unknown polymorphism affording both hexagonal and rectangular 2D networks; (4) extraordinary thermal stability of the organometallic networks. Controlled covalent coupling at the previously Br-functionalized sites was not achieved for the Br4F6BP precursor, in contrast to the comparatively studied non-fluorinated analogue.

Deliberate doping of oxides is a promising route to further optimize their catalytic performance. To guide corresponding experimental endeavours we perform a density-functional theory based computational screening study for a wide range of metal dopant atoms in rutile RuO2. With a focus on the Deacon process, i.e. the catalytic oxidation of HCl to chlorine and water, we use the rate-controlling Cl desorption energy as a reactivity descriptor. As stability descriptors we employ the dopant surface segregation energy, as well as the dopant thermodynamic stability against precipitation into metal or bulk oxide grains. In the oxygen-rich conditions of the Deacon process, particularly the instability against oxide precipitation represents a strong limitation. In this respect, doping with Cu appears as an optimum compromise between stability and catalytic activity enhancement.

Multiphotonenabsorption (MPA) ist eine der wichtigsten nichtlinear-optischen (NLO) Effekte und hat verschiedene Anwendungen, z. B. in Telekommunikation, Verteidigung, Photonik und Biomedizin. Bekannte MPA-Materialien sind Farbstoffe, Quantenpunkte, metallorganische Verbindungen und konjugierte Polymere, die oft als Dispersion in Losung verwendet werden. Wir zeigen wie Metall-organische Gerusteverbindungen (MOFs), eine neuartige NLO-Festkorpermaterialklasse, so entworfen werden konnen, dass sie ausergewohnlich starkes MPA-Verhalten zeigen. MOFs, die aus Zirconium- und Hafnium-Oxo-Clustern bestehen und einen Tetraphenylethen(TPE)-basierten Chromophor-Linker beinhalten, zeigen rekordverdachtig hohe Zweiphotonenabsoptionsquerschnitte mit bis zu 3600 GM. Das einzigartige, modulare Baukastenprinzip von MOFs erlaubt eine Optimierung und Verbesserung ihrer MPA-Eigenschaften, gestutzt auf einen theoriebasierten Ansatz, durch die Berucksichtigung von masgeschneiderter Ladungspolymerisation, konformativer Spannung, dreidimensionaler Anordnung und Ausrichtung der Chromophore im Kristall.

With the advent of efficient electronic structure methods, effective continuum solvation methods have emerged as a way to, at least partially, include solvent effects into simulations without the need for expensive sampling over solvent degrees of freedom. The multipole moment expansion (MPE) model, while based on ideas initially put forward almost 100 years ago, has recently been updated for the needs of modern electronic structure calculations. Indeed, for an all-electron code relying on localized basis sets and-more importantly-a multipole moment expansion of the electrostatic potential, the MPE method presents a particularly cheap way of solving the macroscopic Poisson equation to determine the electrostatic response of a medium surrounding a solute. In addition to our implementation of the MPE model in the FHI-aims electronic structure theory code [ Blum , V. ; Comput. Phys. Commun. 2009 , 180 , 2175 - 2196 , DOI: 10.1016/j.cpc.2009.06.022 ], we describe novel algorithms for determining equidistributed points on the solvation cavity-defined as a charge density isosurface-and the determination of cavity surface and volume from just this collection of points and their local density gradients. We demonstrate the efficacy of our model on an analytically solvable test case, against high-accuracy finite-element calculations for a set of ≈140000 2D test cases, and finally against experimental solvation free energies of a number of neutral and singly charged molecular test sets [ Andreussi , O. ; J. Chem. Phys. 2012 , 136 , 064102 , DOI: 10.1063/1.3676407 ; Marenich , A. V. ; Minnesota Solvation Database , Version 2012; University of Minnesota : Minneapolis, MN, USA , 2012 . ]. In all test cases we find that our MPE approach compares very well with given references at computational overheads20% and sometimes much smaller compared to a plain self-consistency cycle.

We present a method for performing density-functional theory (DFT) calculations in which one or more Kohn-Sham orbitals are constrained to be localized on individual atoms. This constrained-orbital DFT (CO-DFT) approach can be used to tackle two prevalent shortcomings of DFT: the lack of transparency with regard to the governing electronic structure in large (planewave based) DFT calculations and the limitations of semilocal DFT in describing systems with localized electrons or a large degree of static correlation. CO-DFT helps to address the first of these issues by decomposing complex orbital transformations occurring during elementary chemical processes into simpler and more intuitive transformations. The second issue is addressed by using the CO-DFT method to generate configuration states for multiconfiguration Kohn-Sham calculations. We demonstrate both of these applications for elementary reaction steps involved in the oxygen evolution reaction.

The booming field of molecular electronics has fostered a surge of computational research on electronic properties of organic molecular solids. In particular, with respect to a microscopic understanding of transport and loss mechanisms, theoretical studies assume an ever-increasing role. Owing to the tremendous diversity of organic molecular materials, a great number of computational methods have been put forward to suit every possible charge transport regime, material, and need for accuracy. With this review article we aim at providing a compendium of the available methods, their theoretical foundations, and their ranges of validity. We illustrate these through applications found in the literature. The focus is on methods available for organic molecular crystals, but mention is made wherever techniques are suitable for use in other related materials such as disordered or polymeric systems.

Lithium–titanium-oxide (Li4Ti5O12, LTO) is unique among battery materials due to its exceptional cyclability and high rate capability. This performance is believed to derive at least partly from the occupational disorder introduced via mixed Li/Ti occupancy in the LTO spinel-like structure. We explore the vast configuration space accessible during high-temperature LTO synthesis by Monte Carlo sampling and indeed find lowest-energy structures to be characterized by a high degree of microscopic inhomogeneity. Dynamical simulations in corresponding configurations reveal the dominant fraction of Li ions to be immobile on nanosecond time scales. However, Ti antisite-like defects stabilized by the configurational disorder give rise to a novel correlated ion diffusion mechanism. The resulting fast but localized diffusion could be a key element in the sudden rise in conductivity found in LTO in the early stages of charging and questions the validity of ion mobility measurements for this and other configurationall...

We present a generic analysis of the implications of energetic scaling relations on the possibilities for bifunctional gains at homogeneous bimetallic alloy catalysts. Such catalysts exhibit a large number of interface sites, where second-order reaction steps can involve intermediates adsorbed at different active sites. Using different types of model reaction schemes, we show that such site-coupling reaction steps can provide bifunctional gains that allow for a bimetallic catalyst composed of two individually poor catalyst materials to approach the activity of the optimal monomaterial catalyst. However, bifunctional gains cannot result in activities higher than the activity peak of the monomaterial volcano curve as long as both sites obey similar scaling relations, as is generally the case for bimetallic catalysts. These scaling-relation-imposed limitations could be overcome by combining different classes of materials such as metals and oxides.

In a solid-state Li ion battery, the solid-state electrolyte exists principally in regions of high externally applied potentials, and this varies rapidly at the interfaces with electrodes because of the formation of electrochemical double layers. We investigate the implications of these for a model solid-state Li ion Li|Li3OCl|C battery, where C is simply a metallic intercalation cathode. We use density functional theory to calculate the potential dependence of the formation energies of the Li+ charge carriers in superionic Li3OCl. We find that Li+ vacancies are the dominant species at the cathode while Li+ interstitials dominate at the anode. With typical Mg aliovalent doping of Li3OCl, Li+ vacancies dominate the bulk of the electrolyte, as well, with freely mobile vacancies that are only ∼10–4 of the Mg doping density at room temperature. We study the repulsive interaction between Li+ vacancies and find that this is extremely short-range, typically only one lattice constant because of local structural r...

Based on density functional theory, we present a detailed investigation into the on-surface adsorption of atomic oxygen at all three low-index Pd facets in the low-coverage regime. Relying on one consistent computational framework allows for a systematic comparison with respect to surface symmetry, while discerning trends in the adsorption geometries, energies, work functions, and electron densities. We overall find a persisting degree of O-Pd hybridization that is accompanied by minimal charge transfer from the substrate to the adsorbate, thereby resulting in comparable binding energies and diffusion barriers at the three surfaces. Small differences in reactivity are nevertheless reflected in subtle variations of the underlying electronic structure which do not, however, follow the expected order according to atom packing density.

The solution of (generalized) eigenvalue problems for symmetric or Hermitian matrices is a common subtask of many numerical calculations in electronic structure theory or materials science. Depending on the scientific problem, solving the eigenvalue problem can easily amount to a sizeable fraction of the whole numerical calculation, and quite often is even the dominant part by far. For researchers in the field of computational materials science, an efficient and scalable solution of the eigenvalue problem is thus of major importance. The ELPA-library (Eigenvalue SoLvers for Petaflop-Applications) is a well-established dense direct eigenvalue solver library, which has proven to be very efficient and scalable up to very large core counts. It is in a wide-spread production use on a large variety of HPC systems worldwide, and is applied by many codes in the field of materials science. In this paper, we describe the latest optimizations of the ELPA-library for new HPC architectures of the Intel Skylake processor family with an AVX-512 SIMD instruction set, or for HPC systems accelerated with recent GPUs. Apart from those direct hardware-related optimizations, we also describe a complete redesign of the API in a modern modular way, which, apart from a much simpler and more flexible usability, leads to a new path to access system-specific performance optimizations. In order to ensure optimal performance for a particular scientific setting or a specific HPC system, the new API allows the user to influence in a straightforward way the internal details of the algorithms and of performance-critical parameters used in the ELPA-library. On top of that, we introduce an autotuning functionality, which allows for finding the best settings in a self-contained automated way, without the need of much user effort. In situations where many eigenvalue problems with similar settings have to be solved consecutively, the autotuning process of the ELPA-library can be done “on-the-fly”, without the need of preceding the simulation with an “artificial” autotuning step. Practical applications from materials science which rely on reaching a numerical convergence limit by so-called self-consistency iterations can profit from the on-the-fly autotuning. On some examples of scientific interest, simulated with the FHI-aims application, the advantages of the latest optimizations of the ELPA-library are demonstrated.

Modern materials discovery and design studies often rely on the computational screening of large databases. Complementing experimental databases, virtual databases are thereby increasingly established through the first-principles calculation of computationally inexpensive, but for a given application, decisive microscopic quantities of the system. These so-called descriptors are calculated for vast numbers of candidate materials. In general, the sheer volume of datapoints generated in such studies precludes an in depth human analysis. To this end, smart visualization techniques, based e.g., on so-called chemical space networks (CSN), have been developed to extract general design rules connecting structural modifications to changes in the target functionality. In this work, we generate and visualize the CSN of possible crystalline organic semiconductors based on an in-house database of > 64,000 molecular crystals that we extracted from the exhaustive Cambridge Structural Database and for which we computed prominent charge-mobility descriptors. Our CSN thereby links clusters of molecular crystals based on the chemical similarity of the scaffolds of their molecular building blocks and thus groups communities of similar molecules. Including each cluster’s median descriptor values, the CSN visualization not only reproduces known trends of good organic semiconductors but also allows us to extract general design rules for organic molecular scaffolds. Finally, the local environment of each scaffold in our visualization shows how thoroughly its local chemical space has already been explored synthetically. Of special interest here are those clusters with promising descriptor values, yet with little or no connections in the sampled chemical space, as these offer the most room for scaffold optimization.

In computer simulations of solvation effects on chemical reactions, continuum modeling techniques regain popularity as a way to efficiently circumvent an otherwise costly sampling of solvent degrees of freedom. As effective techniques, such implicit solvation models always depend on a number of parameters that need to be determined earlier. In the past, the focus lay mostly on an accurate parametrization of water models. Yet, non-aqueous solvents have recently attracted increasing atten- tion, in particular, for the design of battery materials. To this end, we present a systematic parametrization protocol for the Self-Consistent Continuum Solvation (SCCS) model resulting in optimized parameters for 67 non-aqueous solvents. Our parametrization is based on a collection of ≈6000 experimentally measured partition coefficients, which we collected in the Solv@TUM database presented here. The accuracy of our optimized SCCS model is comparable to the well-known universal continuum solvation model (SMx) family of methods, while relying on only a single fit parameter and thereby largely reducing sta- tistical noise. Furthermore, slightly modifying the non-electrostatic terms of the model, we present the SCCS-P solvation model as a more accurate alternative, in particular, for aromatic solutes. Finally, we show that SCCS parameters can, to a good degree of accuracy, also be predicted for solvents outside the database using merely the dielectric bulk permittivity of the solvent of choice.

The reaction of small cationic tantalum clusters (Tan+, n = 4-8) with molecular oxygen is studied under multi-collision conditions in the gas phase, and the reaction kinetics are analyzed in order to elucidate underlying mechanisms. Reaction pathways as well as relevant apparent rate constants are reported. Two principal pathways in the consecutive oxidation reaction are present in this size regime: solely oxidative degradation (loss of a TaO fragment) in the beginning followed by parallel intact oxidation of certain intermediate species. Selected product structures and energies are subsequently determined via density functional theory calculations. The branching between oxidative fragmentation and intact oxidation is related to the corresponding relaxation dynamics.

We use the gauge-including projector augmented-waves (GIPAW) method to report, for the first time, theoretical 27Al Knight shifts in metallic systems other than metallic Al. We consider metallic Al and a set of six intermetallic compounds, for which experimental chemical shifts were recently made available in the literature. The orbital and spin components of the chemical shielding tensors are computed from the same ground-state spin-polarized electronic structure, converged under the influence of a uniform external magnetic field. A linear response formalism is used to compute the orbital part, while the spin part is approximated by the linear relationship between the external field and the Fermi contact contribution to the induced magnetic hyperfine field at the nuclear position. Core spin-polarization effects are taken into account by means of a perturbative approach. Our results show that the GIPAW approach yields reasonably acceptable chemical shifts with affordable k-meshes in the irreducible Brillo...

The correlation between in vitro dissolution rates and the efficiency of drug formulations establishes an opportunity for accelerated drug development. Using in silico methods to predict the dissolution rates bears the prospect of further efficiency gains by avoiding the actual synthesis of candidate formulations. Here, we present a computational protocol that achieves such prediction for molecular crystals at low undersaturation. The protocol exploits the classic spiral dissolution model to minimize the number of material parameters that require explicit molecular simulations. Comparison to available data for acetylsalicylic acid and alpha lactose monohydrate indicates a tunable accuracy within one order of magnitude.

Oxide formation under oxygen-rich reaction conditions has independently been reported for both CO oxidation and NO oxidation with Pd single-crystal model catalysts. We present a first-principles kinetic Monte Carlo study addressing the simultaneous occurrence of both reactions at Pd(100) exposed to CO- and NO-containing feeds. Even in most oxygen-rich feeds, very small amounts of NO are found to reduce the surface oxygen coverage well below the level required to induce oxide formation. Even though NO and CO compete for the same surface sites and surface oxygen, the ongoing NO oxidation reactions furthermore lead to a partially strong enhancement of the CO oxidation activity. This highlights synergistic effects of multicomponent gas feeds on both surface composition and catalytic activity that cannot be captured, nor extrapolated from prevalent studies focusing on individual reactions.

Controllable synthesis of defect-free graphene is crucial for applications since the properties of graphene are highly sensitive to any deviations from the crystalline lattice. We focus here on the emerging use of liquid Cu catalysts, which has high potential for fast and efficient industrial-scale production of high-quality graphene. The interface between graphene and liquid Cu is studied using force field and ab initio molecular dynamics, revealing a complete or partial embedding of finite-sized flakes. By analyzing flakes of different sizes we find that the size-dependence of the embedding can be rationalized based on the energy cost of embedding versus bending the graphene flake. The embedding itself is driven by the formation of covalent bonds between the under-coordinated edge C atoms and the liquid Cu surface, which is accompanied by a significant charge transfer. In contrast, the central flake atoms are located around or slightly above 3 {\AA} from the liquid Cu surface and exhibit weak vdW-bonding and much lower charge transfer. The structural and electronic properties of the embedded state revealed in our work provides the atomic-scale information needed to develop effective models to explain the special growth observed in experiments where various interesting phenomena such as flake self-assembly and rotational alignment, high growth speeds and low defect densities in the final graphene product have been observed., Comment: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in J. Chem. Phys. 153, 074702 (2020) and may be found at https://doi.org/10.1063/5.0020126

Interfacial electrochemistry and photo(electro)catalysis are key processes that convert the energy of photons or electrons to chemical bonds in many energy conversion and storage technologies. Achieving a molecular level understanding of the funda- mental interfacial structure, energetics, dynamics, and reaction mechanisms that govern these processes represents a broad frontier for chemical physics and physical chemistry. This Special Topic contains a collection of articles that range from the development of new experimental and computational techniques to the novel application of those techniques for mechanistic studies, as the principal investigators seek a fundamental molecular understanding of both electrode/electrolyte interfaces and the relevant electrocatalytic, photocatalytic, and photoelectrochemical reactions taking place thereabout. Altogether, this col- lection of articles captures the current state of this very active, frontier research field and highlights the current and remaining key scientific challenges and opportunities.

Computational screening for new and improved catalyst materials relies on accurate and low-cost predictions of key parameters such as adsorption energies. Here, we use recently developed compressed sensing methods to identify descriptors whose predictive power extends over a wide range of adsorbates, multi-metallic transition metal surfaces and facets. The descriptors are expressed as non-linear functions of intrinsic properties of the clean catalyst surface, e.g. coordination numbers, d-band moments and density of states at the Fermi level. From a single density-functional theory calculation of these properties, we predict adsorption energies at all potential surface sites, and thereby also the most stable geometry. Compared to previous approaches such as scaling relations, we find our approach to be both more general and more accurate for the prediction of adsorption energies on alloys with mixed-metal surfaces, already when based on training data including only pure metals. This accuracy can be systematically improved by adding also alloy adsorption energies to the training data., Comment: This document is the unedited Author's version of a Submitted Work that was subsequently accepted for publication in ACS Catalysis, copyright American Chemical Society after peer review. To access the final edited and published work see https://pubs.acs.org/doi/10.1021/acscatal.8b04478