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The computational discovery of novel materials has been one of the main motivations behind research in theoretical chemistry for several decades. Despite much effort, this is far from a solved problem, however. Among other reasons, this is due to the enormous space of possible structures and compositions that could potentially be of interest. In the case of inorganic materials, this is exacerbated by the combinatorics of the periodic table, since even a single crystal structure can in principle display millions of compositions. Consequently, there is a need for tools that enable a more guided exploration of the materials design space. Here, generative machine learning (ML) models have recently emerged as a promising technology. In this work, we assess the performance of a range of deep generative models based on Reinforcement Learning (RL), Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN) for the prototypical case of designing Elpasolite compositions with low formation energies. By relying on the fully enumerated space of 2~million main group Elpasolites, the precision, coverage and diversity of the generated materials is rigorously assessed. Additionally, a hyperparameter selection scheme for generative models in chemical composition space is developed.

Hydrogenated graphene (H-Gr) is an extensively studied system not only because of its capabilities as a simplified model system for hydrocarbon chemistry but also because hydrogenation is a compelling method for Gr functionalization. However, knowledge of how H-Gr interacts with molecules at higher pressures and ambient conditions is lacking. Here we present experimental and theoretical evidence that room temperature O2 exposure at millibar pressures leads to preferential removal of H dimers on H-functionalized graphene, leaving H clusters on the surface. Our density functional theory (DFT) analysis shows that the removal of H dimers is the result of water or hydrogen peroxide formation. For water formation, we show that the two H atoms in the dimer motif attack one end of the physisorbed O2 molecule. Moreover, by comparing the reaction pathways in a vacuum with the ones on free-standing graphene and on the graphene/Ir(111) system, we find that the main role of graphene is to arrange the H atoms in geometrical positions, which facilitates the activation of the O═O double bond.

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 in heterogeneous catalysis relies increasingly on machine learning models for predicting key input parameters due to the high cost of computing these directly using first-principles methods. This becomes especially relevant when considering complex materials spaces, e.g. alloys, or complex reaction mechanisms with adsorbates that may exhibit bi- or higher-dentate adsorption motifs. Here we present a data-efficient approach to the prediction of binding motifs and associated adsorption enthalpies of complex adsorbates at transition metals (TMs) and their alloys based on a customized Wasserstein Weisfeiler-Lehman graph kernel and Gaussian Process Regression. The model shows good predictive performance, not only for the elemental TMs on which it was trained, but also for an alloy based on these TMs. Furthermore, incorporation of minimal new training data allows for predicting an out-of-domain TM. We believe the model may be useful in active learning approaches, for which we present an ensemble uncertainty estimation approach., update open source repository and reference

Potential dependencies of adsorption energies at metal electrodes are often interpreted in terms of a dipole-field interaction picture. Classical electrostatics would then suggest particularly strong dependencies at protruding defect sites like steps and kinks due to a local field enhancement. Here, we use first-principles density functional theory calculations that capture the capacitive charging of the electric double layer to analyze these dependencies for prototypical adsorbates at vicinal Pt(111) surfaces in an implicit aqueous electrolyte. This analysis confirms effective dipole-field-type dependencies but rationalizes why they can neither be estimated on the basis of accessible molecular dipoles nor the mere atomic-scale geometry of the adsorption site. The observed magnitude questions mechanistic analyses of surface catalytic reactions made on the basis of the prevalent computational hydrogen electrode approach that is agnostic to these potential-induced adsorption energy variations.

The discovery of new catalytically active materials is one of the holy grails of computational chemistry as it has the potential to accelerate the adoption of renewable energy sources and reduce the energy consumption of chemical industry. Indeed, heterogeneous catalysis is essential for the production of synthetic fuels and many commodity chemicals. Consequently, novel solid catalysts with higher activity and selectivity, increased sustainability and longevity, or improved prospects for rejuvenation and cyclability are needed for a diverse range of processes. Unfortunately, computational catalyst discovery is a daunting task, among other reasons because it is often unclear whether a proposed material is stable or synthesizable. This perspective proposes a new approach to this challenge, namely the use of generative grammars. We outline how grammars can guide the search for stable catalysts in a large chemical space and sketch out several research directions that would make this technology applicable to real materials.

Data-driven approaches based on high-throughput capabilities and machine learning hold promise in revolutionizing human-centred materials discovery for sustainability and decarbonization. This Review examines the strengths and limitations of different traditional and emerging approaches to demonstrate their inherent connection and highlight the evolving paradigms of materials design., Breakthroughs in molecular and materials discovery require meaningful outliers to be identified in existing trends. As knowledge accumulates, the inherent bias of human intuition makes it harder to elucidate increasingly opaque chemical and physical principles. Moreover, given the limited manual and intellectual throughput of investigators, these principles cannot be efficiently applied to design new materials across a vast chemical space. Many data-driven approaches, following advances in high-throughput capabilities and machine learning, have tackled these limitations. In this Review, we compare traditional, human-centred methods with state-of-the-art, data-driven approaches to molecular and materials discovery. We first introduce the limitations of human-centred Edisonian, model-system and descriptor-based approaches. We then discuss how data-driven approaches can address these limitations by promoting throughput, reducing cognitive overload and biases, and establishing atomistic understanding that is transferable across a broad chemical space. We examine how high-throughput capabilities can be combined with active learning and inverse design to efficiently optimize materials out of millions or an intractable number of candidates. Lastly, we pinpoint challenges to accelerate future workflows and ultimately enable self-driving platforms, which automate and streamline the optimization of molecules and materials in iterative cycles.

Machine-learning interatomic potentials like Gaussian Approximation Potentials (GAPs) constitute a powerful class of surrogate models to computationally involved first-principles calculations. At similar predictive quality but significantly reduced cost, they could leverage otherwise barely tractable extensive sampling as in global surface structure determination (SSD). This efficiency is jeopardized though, if an a priori unknown structural and chemical search space as in SSD requires an excessive number of first-principles data for the GAP training.To this end, we present a general and data-efficient iterative training protocol that blends the creation of new training data with the actual surface exploration process. Demonstrating this protocol with the SSD of low-index facets of rutile IrO2 and RuO2 , the involved simulated annealing on the basis of the refining GAP identifies a number of unknown terminations even in the restricted sub-space of (1×1) surface unit-cells. Especially in an O-poor environment, some of these, then metal-rich terminations, are thermodynamically most stable and are reminiscent of complexions as discussed for complex ceramic materials.

Theoretical calculations of core electron binding energies are required for the interpretation of experimental X-ray photoelectron spectra, but achieving accurate results for solids has proven difficult. In this work, we demonstrate that accurate absolute core electron binding energies in both metallic and insulating solids can be obtained from periodic all-electron Δ-self-consistent-field (ΔSCF) calculations. In particular, we show that core electron binding energies referenced to the valence band maximum can be obtained as total energy differences between two (N – 1)-electron systems: one with a core hole and one with an electron removed from the highest occupied valence state. To achieve convergence with respect to the supercell size, the analogy between localized core holes and charged defects is exploited. Excellent agreement between calculated and experimental core electron binding energies is found for both metals and insulators, with a mean absolute error of 0.24 eV for the systems considered.

Advanced energy materials 11(26), 2100707 (2021). doi:10.1002/aenm.202100707, Published by Wiley-VCH, Weinheim

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

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.

Electrosorption of solvated species at metal electrodes is a most fundamental class of processes in interfacial electrochemistry. Here, we use its sensitive dependence on the electric double layer to assess the performance of ab initio thermodynamics approaches increasingly used for the first-principles description of electrocatalysis. We show analytically that computational hydrogen electrode calculations at zero net-charge can be understood as a first-order approximation to a fully grand canonical approach. Notably, higher-order terms in the applied potential caused by the charging of the double layer include contributions from adsorbate-induced changes in the work function and in the interfacial capacitance. These contributions are essential to yield prominent electrochemical phenomena such as non-Nernstian shifts of electrosorption peaks and non-integer electrosorption valencies. We illustrate this by calculating peak shifts for H on Pt electrodes and electrosorption valencies of halide ions on Ag electrodes, obtaining qualitative agreement with experimental data already when considering only second order terms. The results demonstrate the agreement between classical electrochemistry concepts and a first-principles fully grand canonical description of electrified interfaces and shed new light on the widespread computational hydrogen electrode approach.

Machine learning (ML) models are increasingly used in combination with electronic structure calculations to predict molecular properties at a much lower computational cost in high-throughput settings. Such ML models require representations that encode the molecular structure, which are generally designed to respect the symmetries and invariances of the target property. However, size-extensivity is usually not guaranteed for so-called global representations. In this contribution, we show how extensivity can be built into global ML models using, e. g., the Many-Body Tensor Representation. Properties of extensive and non-extensive models for the atomization energy are systematically explored by training on small molecules and testing on small, medium and large molecules. Our results show that non-extensive models are only useful in the size-range of their training set, whereas extensive models provide reasonable predictions across large size differences. Remaining sources of error for extensive models are discussed.

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.

This review article is intended as a practical guide for newcomers to the field of kinetic Monte Carlo (KMC) simulations, and specifically to lattice KMC simulations as prevalently used for surface and interface applications. We will provide worked out examples using the kmos code, where we highlight the central approximations made in implementing a KMC model as well as possible pitfalls. This includes the mapping of the problem onto a lattice and the derivation of rate constant expressions for various elementary processes. Example KMC models will be presented within the application areas surface diffusion, crystal growth and heterogeneous catalysis, covering both transient and steady-state kinetics as well as the preparation of various initial states of the system. We highlight the sensitivity of KMC models to the elementary processes included, as well as to possible errors in the rate constants. For catalysis models in particular, a recurrent challenge is the occurrence of processes at very different timescales, e.g. fast diffusion processes and slow chemical reactions. We demonstrate how to overcome this timescale disparity problem using recently developed acceleration algorithms. Finally, we will discuss how to account for lateral interactions between the species adsorbed to the lattice, which can play an important role in all application areas covered here., This document is the final Author's version of a manuscript that has been peer reviewed and accepted for publication in Frontiers in Chemistry. To access the final edited and published work see https://www.frontiersin.org/articles/10.3389/fchem.2019.00202/abstract

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.

The local structure of the nonplanar phthalocyanine, vanadyl phthalocyanine (VOPc), adsorbed on Cu(111) at a coverage of approximately one-half of a saturated molecular layer, has been investigated by a combination of normal-incidence X-ray standing waves (NIXSW), scanned-energy mode photoelectron diffraction (PhD), and density-functional theory (DFT), complemented by scanning tunnelling microscopy (STM). Qualitative assessment of the NIXSW data clearly shows that both “up” and “down” orientations of the molecule (with V═O pointing out of, and into, the surface) must coexist on the surface. O 1s PhD proves to be inconclusive regarding the molecular orientation. DFT calculations, using two different dispersion correction schemes, show good quantitative agreement with the NIXSW structural results for equal co-occupation of the two different molecular orientations and clearly favor the many body dispersion (MBD) method to deal with long-range dispersion forces. The calculated relative adsorption energies of the differently oriented molecules at the lowest coverage show a strong preference for the “up” orientation, but at higher local coverages, this energetic difference decreases, and mixed orientation phases are almost energetically equivalent to pure “up”-oriented phases. DFT-based Tersoff–Hamann simulations of STM topographs for the two orientations cast some light on the extent to which such images provide a reliable guide to molecular orientation.

In molecular nanotechnology, a single molecule is envisioned to act as the basic building block of electronic devices. Such devices may be of special interest for organic photovoltaics, data storage, and smart materials. However, more often than not the molecular function is quenched upon contact with a conducting support. Trial-and-error-based decoupling strategies via molecular functionalisation and change of substrate have in many instances proven to yield unpredictable results. The adsorbate-substrate interactions that govern the function can be understood with the help of first-principles simulation. Employing dispersion-corrected Density-Functional Theory (DFT) and linear expansion Delta-Self-Consistent-Field DFT, the electronic structure of a prototypical surface-adsorbed functional molecule, namely azobenzene adsorbed to (111) single crystal facets of copper, silver and gold, is investigated and the main reasons for the loss or survival of the switching function upon adsorption are identified. The light-induced switching ability of a functionalised derivative of azobenzene on Au(111) and azobenzene on Ag(111) and Au(111) is assessed based on the excited-state potential energy landscapes of their transient molecular ions, which are believed to be the main intermediates of the experimentally observed isomerisation reaction. We provide a rationalisation of the experimentally observed function or lack thereof that connects to the underlying chemistry of the metal-surface interaction and provides insights into general design strategies for complex light-driven reactions at metal surfaces., 14 pages, 5 figures, submitted to J. Phys. Condens. Matter

We present Genarris, a Python package that performs configuration space screening for molecular crystals of rigid molecules by random sampling with physical constraints. For fast energy evaluations Genarris employs a Harris approximation, whereby the total density of a molecular crystal is constructed via superposition of single molecule densities. Dispersion-inclusive density functional theory (DFT) is then used for the Harris density without performing a self-consistency cycle. Genarris uses machine learning for clustering, based on a relative coordinate descriptor (RCD) developed specifically for molecular crystals, which is shown to be robust in identifying packing motif similarity. In addition to random structure generation, Genarris offers three workflows based on different sequences of successive clustering and selection steps: the "Rigorous" workflow is an exhaustive exploration of the potential energy landscape, the "Energy" workflow produces a set of low energy structures, and the "Diverse" workflow produces a maximally diverse set of structures. The latter is recommended for generating initial populations for genetic algorithms. Here, the implementation of Genarris is reported and its application is demonstrated for three test cases.

Atomic scale switches working at room temperature represent the ultimate level of device miniaturization. Using scanning tunneling microscopy, we find a bistable switching between two mirror-symmetric configurations of self-assembled magic rare earth silicide clusters on the Si(111) 7 X 7 surface. Density functional theory reveals an energy barrier of 1.3 eV between the two cluster configurations, suppressing the switching even at room temperature. However, intentional switch- ing between the two states is possible in the presence of a close tunneling tip due to a tip-induced lowering of the energy barrier.

Reactivity at metal-oxide interfaces is of fundamental importance in heterogeneous catalysis. Herein, we report a thorough surface-science study on the growth and chemical activity of ultrathin ZnO...

Conversion of energy at the gas–solid interface lies at the heart of many industrial applications such as heterogeneous catalysis. Dissipation of parts of this energy into the substrate bulk drives the thermalization of surface species, but also constitutes a potentially unwanted loss channel. At present, little is known about the underlying microscopic dissipation mechanisms and their (relative) efficiency. At metal surfaces, prominent such mechanisms are the generation of substrate phonons and the electronically non-adiabatic excitation of electron–hole pairs. In recent years, dedicated surface science experiments at defined single-crystal surfaces and predictive-quality first-principles simulations have increasingly been used to analyze these dissipation mechanisms in prototypical surface dynamical processes such as gas-phase scattering and adsorption, diffusion, vibration, and surface reactions. In this topical review we provide an overview of modeling approaches to incorporate dissipation into corresponding dynamical simulations starting from coarse-grained effective theories to increasingly sophisticated methods. We illustrate these at the level of individual elementary processes through applications found in the literature, while specifically highlighting the persisting difficulty of gauging their performance based on experimentally accessible observables.

Many problems in computational materials science and chemistry require the evaluation of expensive functions with locally rapid changes, such as the turn-over frequency of first principles kinetic Monte Carlo models for heterogeneous catalysis. Because of the high computational cost, it is often desirable to replace the original with a surrogate model, e.g., for use in coupled multiscale simulations. The construction of surrogates becomes particularly challenging in high-dimensions. Here, we present a novel version of the modified Shepard interpolation method which can overcome the curse of dimensionality for such functions to give faithful reconstructions even from very modest numbers of function evaluations. The introduction of local metrics allows us to take advantage of the fact that, on a local scale, rapid variation often occurs only across a small number of directions. Furthermore, we use local error estimates to weigh different local approximations, which helps avoid artificial oscillations. Finally, we test our approach on a number of challenging analytic functions as well as a realistic kinetic Monte Carlo model. Our method not only outperforms existing isotropic metric Shepard methods but also state-of-the-art Gaussian process regression.

First-principles screening studies aimed at predicting the catalytic activity of transition metal (TM) catalysts have traditionally been based on mean-field (MF) microkinetic models, which neglect the effect of spatial correlations in the adsorbate layer. Here we critically assess the accuracy of such models for the specific case of CO methanation over stepped metals by comparing to spatially resolved kinetic Monte Carlo (kMC) simulations. We find that the typical low diffusion barriers offered by metal surfaces can be significantly increased at step sites, which results in persisting correlations in the adsorbate layer. As a consequence, MF models may overestimate the catalytic activity of TM catalysts by several orders of magnitude. The potential higher accuracy of kMC models comes at a higher computational cost, which can be especially challenging for surface reactions on metals due to a large disparity in the time scales of different processes. In order to overcome this issue, we implement and test a recently developed algorithm for achieving temporal acceleration of kMC simulations. While the algorithm overall performs quite well, we identify some challenging cases which may lead to a breakdown of acceleration algorithms and discuss possible directions for future algorithm development.

X-ray absorption spectroscopy yields direct access to the electronic and geometric structure of hybrid inorganic-organic interfaces formed upon adsorption of complex molecules at metal surfaces. The unambiguous interpretation of corresponding spectra is challenged by the intrinsic geometric flexibility of the adsorbates and the chemical interactions with the interface. Density-functional theory (DFT) calculations of the extended adsorbate-substrate system are an established tool to guide peak assignment in X-ray photoelectron spectroscopy (XPS) of complex interfaces. We extend this to the simulation and interpretation of X-ray absorption spectroscopy (XAS) data in the context of functional organic molecules on metal surfaces using dispersion-corrected DFT calculations within the transition potential approach. On the example of X-ray absorption signatures for the prototypical case of 2H-porphine adsorbed on Ag(111) and Cu(111) substrates, we follow the two main effects of the molecule/surface interaction on XAS: (1) the substrate-induced chemical shift of the 1s core levels that dominates in physisorbed systems and (2) the hybridization-induced broadening and loss of distinct resonances that dominates in more chemisorbed systems., 13 pages, 4 figures

First-principles screening approaches exploiting energy trends in surface adsorption represent an unparalleled success story in recent computational catalysis research. Here we argue that our still limited understanding of the structure of active sites is one of the major bottlenecks towards an ever extended and reliable use of such computational screening for catalyst discovery. For low-index transition metal surfaces, the prevalently chosen high-symmetry (terrace and step) sites offered by the nominal bulk-truncated crystal lattice might be justified. For more complex surfaces and composite catalyst materials, computational screening studies will need to actively embrace a considerable uncertainty with respect to what truly are the active sites. By systematically exploring the space of possible active site motifs, such studies might eventually contribute towards a targeted design of optimized sites in future catalysts.

In the future, (electro‐)chemical catalysts will have to be more tolerant towards a varying supply of energy and raw materials. This is mainly due to the fluctuating nature of renewable energies. For example, power‐to‐chemical processes require a shift from steady‐state operation towards operation under dynamic reaction conditions. This brings along a number of demands for the design of both catalysts and reactors, because it is well‐known that the structure of catalysts is very dynamic. However, in‐depth studies of catalysts and catalytic reactors under such transient conditions have only started recently. This requires studies and advances in the fields of 1) operando spectroscopy including time‐resolved methods, 2) theory with predictive quality, 3) kinetic modelling, 4) design of catalysts by appropriate preparation concepts, and 5) novel/modular reactor designs. An intensive exchange between these scientific disciplines will enable a substantial gain of fundamental knowledge which is urgently required. This concept article highlights recent developments, challenges, and future directions for understanding catalysts under dynamic reaction conditions.

We augment ab initio molecular dynamics simulations with a quantitative account of phononic dissipation to study the non-equilibrium aftermath of the exothermic oxygen dissociation at low-index (111), (100), and (110) Pd surfaces. Comparing the hyperthermal diffusion arising from a non-instantaneous dissipation of the released chemical energy, we find a striking difference in the resulting "hot" adatom lifetime that is not overall reflected in experimentally recorded product end distances. We rationalize this finding through a detailed mode-specific phonon analysis and identify the dominant dissipation channels as qualitatively different groups of localized surface modes that ultimately lead to intrinsically different rates of dissipation to the Pd bulk. The thus obtained first-principles perspective on non-equilibrium adsorbate-phonon dynamics thereby underscores the sensitive dependence on details of the phononic fine structure, while questioning prevalent assumptions about energy sinks made in commonly used model bath Hamiltonians.

Blue-color stimulated emission with low threshold power is observed from In- and Zn-MOFs, which feature a highly fluorescent chromophore densely packed and rigidly linked to the metal-ion centers in the solid state. The density-of-states and transition dipole moments are calculated and the stimulated emission phenomenon is correlated with these properties. MOE (Min. of Education, S’pore) Accepted version

Multi‐photon absorption (MPA) is among the most prominent nonlinear optical (NLO) effects and has applications, for example in telecommunications, defense, photonics, and bio‐medicines. Established MPA materials include dyes, quantum dots, organometallics and conjugated polymers, most often dispersed in solution. We demonstrate how metal–organic frameworks (MOFs), a novel NLO solid‐state materials class, can be designed for exceptionally strong MPA behavior. MOFs consisting of zirconium‐ and hafnium‐oxo‐clusters and featuring a chromophore linker based on the tetraphenylethene (TPE) molecule exhibit record high two‐photon absorption (2PA) cross‐section values, up to 3600 GM. The unique modular building‐block principle of MOFs allows enhancing and optimizing their MPA properties in a theory‐guided approach by combining tailored charge polarization, conformational strain, three‐dimensional arrangement, and alignment of the chromophore linkers in the crystal. MOE (Min. of Education, S’pore) Accepted version

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and `best practices´ for performing CSP calculations. All of the targets, apart from a single potentially disordered Z?? = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms. Fil: Reilly, Anthony M.. Cambridge Crystallographic Data Centre; Fil: Cooper, Richard I.. Chemistry Research Laboratory; Fil: Adjiman, Claire S.. Imperial College London; Reino Unido Fil: Bhattacharya, Saswata. Fritz Haber Institute Of The Max Planck Society; Fil: Boese, A. Daniel. Karl-franzens-universitat Graz; Austria Fil: Brandenburg, Jan Gerit. Colegio Universitario de Londres; Reino Unido. Universitat Bonn; Alemania Fil: Bygrave, Peter J.. University of Southampton; Reino Unido Fil: Bylsma, Rita. Radboud Universiteit Nijmegen; Países Bajos Fil: Campbell, Josh E.. University of Southampton; Reino Unido Fil: Car, Roberto. University of Princeton; Estados Unidos Fil: Case, David H.. University of Southampton; Reino Unido Fil: Chadha, Renu. University Institute Of Pharmaceutical Sciences India; India Fil: Cole, Jason C.. Cambridge Crystallographic Data Centre; Fil: Cosburn, Katherine. University of Tulane; Estados Unidos. University of Toronto; Canadá Fil: Cuppen, Herma M.. Radboud Universiteit Nijmegen; Países Bajos Fil: Curtis, Farren. University of Tulane; Estados Unidos. University of Carnegie Mellon; Estados Unidos Fil: Day, Graeme M.. University of Southampton; Reino Unido Fil: DiStasio, Robert A.. University of Princeton; Estados Unidos. Cornell University; Estados Unidos Fil: Dzyabchenko, Alexander. Karpov Institute Of Physical Chemistry; Fil: Van Eijck, Bouke P.. University of Utrecht; Países Bajos. Utrecht University; Países Bajos Fil: Elking, Dennis M.. Openeye Scientific Software, Inc; Fil: Van Den Ende, Joost A.. Radboud Universiteit Nijmegen; Países Bajos Fil: Facelli, Julio C.. University of Utah; Estados Unidos Fil: Ferraro, Marta B.. Universidad de Buenos Aires; Argentina Fil: Fusti-Molnar, Laszlo. Openeye Scientific Software, Inc; Fil: Gatsiou, Christina-Anna. Imperial College London; Reino Unido Fil: Gee, Thomas S.. University of Southampton; Reino Unido Fil: De Gelder, René. Radboud Universiteit Nijmegen; Países Bajos Fil: Ghiringhelli, Luca M.. Fritz Haber Institute Of The Max Planck Society; Fil: Goto, Hitoshi. Toyohashi University Of Technology; Fil: Grimme, Stefan. Universitat Bonn; Alemania Fil: Guo, Rui. Colegio Universitario de Londres; Reino Unido Fil: Hofmann, Detlef W. M.. Flexcryst; . Polaris; Fil: Hoja, Johannes. Fritz Haber Institute Of The Max Planck Society; Fil: Hylton, Rebecca K.. Colegio Universitario de Londres; Reino Unido Fil: Iuzzolino, Luca. Colegio Universitario de Londres; Reino Unido Fil: Jankiewicz, Wojciech. University Of Silesia In Katowice; Fil: De Jong, Daniël T.. 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The size-modified Poisson-Boltzmann (MPB) equation is an efficient implicit solvation model which also captures electrolytic solvent effects. It combines an account of the dielectric solvent response with a mean-field description of solvated finite-sized ions. We present a general solution scheme for the MPB equation based on a fast function-space oriented Newton method and a Green's function preconditioned iterative linear solver. In contrast to popular multi-grid solvers this approach allows to fully exploit specialized integration grids and optimized integration schemes. We describe a corresponding numerically efficient implementation for the full-potential density-functional theory (DFT) code FHI-aims. We show that together with an additional Stern layer correction the DFT+MPB approach can describe the mean activity coefficient of a KCl aqueous solution over a wide range of concentrations. The high sensitivity of the calculated activity coefficient on the employed ionic parameters thereby suggests to use extensively tabulated experimental activity coefficients of salt solutions for a systematic parametrization protocol., Comment: Just accepted at Journal of Chemical Theory and Computation. This document is the unedited Authors version of a Submitted Work that was subsequently accepted for publication in the Journal of Chemical Theory and Computation, copyright American Chemical Society after peer review. To access the final edited and published work see http://pubs.acs.org/doi/abs/10.1021/acs.jctc.6b00435

Efficient structure search is a major challenge in computational materials science. We present a modification of the basin hopping global geometry optimization approach that uses a curvilinear coordinate system to describe global trial moves. This approach has recently been shown to be efficient in structure determination of clusters [Nano Letters 15, 8044-8048 (2015)] and is here extended for its application to covalent, complex molecules and large adsorbates on surfaces. The employed, automatically constructed delocalized internal coordinates are similar to molecular vibrations, which enhances the generation of chemically meaningful trial structures. By introducing flexible constraints and local translation and rotation of independent geometrical subunits we enable the use of this method for molecules adsorbed on surfaces and interfaces. For two test systems, trans-$\beta$-ionylideneacetic acid adsorbed on a Au(111) surface and methane adsorbed on a Ag(111) surface, we obtain superior performance of the method compared to standard optimization moves based on Cartesian coordinates., Comment: 14 pages, 9 figures