Selective dimethylformamide formation occurs over PdAu; reactivity and selectivity are sensitive to Pd : Au ratio. Reaction kinetics suggest a crowded surface and that beneficial effects of surface hydroxyls are induced by co-feeding water.

Electrochemical scanning tunneling microscopy (EC-STM) and electrochemical quartz crystal microbalance (E-QCM) techniques in combination with DFT calculations have been applied to reveal the static phase and the phase transition of copper underpotential deposition (UPD) on a gold electrode surface. EC-STM demonstrated, for the first time, the direct visualization of the disintegration of (root 3 x root 3)R30 degrees copper UPD adlayer with coadsorbed SO42- while changing sample potential (E-S) toward the redox Pa2/Pc2 peaks, which are associated with the phase transition between the Cu UPD (root 3 x root 3)R30 degrees phase II and disordered randomly adsorbed phase III. DFT calculations show that SO42- binds via three oxygens to the bridge sites of the copper with sulfate being located directly above the copper vacancy in the (root 3 x root 3)R30 degrees adlayer, whereas the remaining oxygen of the sulfate points away from the surface. E-QCM measurement of the change of the electric charge due to Cu UPD Faradaic processes, the change of the interfacial mass due to the adsorption and desorption of Cu(II) and SO42-, and the formation and stripping of UPD copper on the gold surface provide complementary information that validates the EC-STM and DFT results. This work demonstrated the advantage of using complementary in situ experimental techniques (E-QCM and EC-STM) combined with simulations to obtain an accurate and complete picture of the dynamic interfacial adsorption and UPD processes at the electrode/electrolyte interface.

Machine learning interatomic potentials (MLIPs) are an emerging modeling technique that promises to provide electronic structure theory accuracy for a fraction of its cost, however, the transferability of MLIPs is a largely unknown factor. Recently, it has been proposed (J. Chem. Phys., 2023, 158, 084111) that MLIPs trained on solely liquid water data cannot describe vapor-liquid equilibrium while recovering the many-body decomposition analysis of gas-phase water clusters, as MLIPs do not directly learn the physically correct interactions of water molecules, limiting transferability. In this work, we show that MLIPs based on an equivariant neural network architecture trained on only 3,200 bulk liquid water structures reproduces liquid-phase water properties (e.g., density within 0.003 g/cm3 between 230 and 365 K), vapor-liquid equilibrium properties up to 550 K, the many-body decomposition analysis of gas-phase water cluster up to six-body interactions, and the relative energy and the vibrational density of states of ice phases. This study highlights that state-of-the-art MLIPs have the potential to develop transferable models for arbitrary phases of water that remain stable in nanosecond-long simulations.

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE) providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe-Salpeter Equation (BSE), variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn-Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support of GPU acceleration has been achieved with minor modifications of the GPAW code thanks to the CuPy library. We end the review with an outlook describing some future plans for GPAW.

Recent developments in machine learning interatomic potentials (MLIPs) have empowered even nonexperts in machine learning to train MLIPs for accelerating materials simulations. However, reproducibility and independent evaluation of presented MLIP results is hindered by a lack of clear standards in current literature. In this Perspective, we aim to provide guidance on best practices for documenting MLIP use while walking the reader through the development and deployment of MLIPs including hardware and software requirements, generating training data, training models, validating predictions, and MLIP inference. We also suggest useful plotting practices and analyses to validate and boost confidence in the deployed models. Finally, we provide a step-by-step checklist for practitioners to use directly before publication to standardize the information to be reported. Overall, we hope that our work will encourage the reliable and reproducible use of these MLIPs, which will accelerate their ability to make a positive impact in various disciplines including materials science, chemistry, and biology, among others.

Halogen bonding between two negatively charged species, tetraiodo-p-benzoquinone anion radicals (I4 Q-. ) and iodide anions, was observed and characterized for the first time. X-ray structural and EPR/UV-Vis spectral studies revealed that the anion-anion bonding led to the formation of crystals comprising 2D layers of I4 Q-. anion radicals linked by iodides and separated by Et4 N+ counter-ions. Computational analysis suggested that the seemingly antielectrostatic halogen bonds in these systems were formed via a combination of several factors. First, an attenuation of the interionic repulsion by the solvent facilitated close approach of the anions leading to their mutual polarization. This resulted in the appearance of positively charged areas (σ-holes) on the surface of the iodine substituents in I4 Q-. responsible for the attractive interaction. Finally, the solid-state associations were also stabilized by multicenter (4:4) halogen bonding between I4 Q-. and iodide.

Heterogeneous catalysts constitute a crucial component of many industrial processes, and to gain an understanding of the atomic-scale features of such catalysts, ab initio density functional theory is widely employed. Recently, growing computational power has permitted the extension of such studies to complex reaction networks involving either high adsorbate coverages or multidentate adsorbates, which bind to the surface through multiple atoms. Describing all possible adsorbate configurations for such systems, however, is often not possible based on chemical intuition alone. To systematically treat such complexities, we present a generalized Python-based graph theory approach to convert atomic scale models into undirected graph representations. These representations, when combined with workflows such as evolutionary algorithms, can systematically generate high coverage adsorbate models and classify unique minimum energy multidentate adsorbate configurations for surfaces of low symmetry, including multi-elemental alloy surfaces, steps, and kinks. Two case studies are presented which demonstrate these capabilities; first, an analysis of a coverage-dependent phase diagram of absorbate NO on the Pt3Sn(111) terrace surface, and second, an investigation of adsorption energies, together with identifying unique minimum energy configurations, for the reaction intermediate propyne (CHCCH3*) adsorbed on a PdIn(021) step surface. The evolutionary algorithm approach reproduces high coverage configurations of NO on Pt3Sn(111) using only 15% of the number of simulations required for a brute force approach. Furthermore, the screening of potentially hundreds of multidentate adsorbates is shown to be possible without human intervention. The strategy presented is quite general and can be applied to a spectrum of complex atomic systems.

Despite the progress in the study of anion-π interactions, there are still inconsistencies in the use of this term and the experimental data about factors affecting the strength of such bonding are limited. To shed light on these issues, we explored supramolecular associations between NCS- anions and a series of aromatic, olefinic or quinoidal π-acceptors. Combined experimental and computational studies revealed that all these complexes were formed by an attraction of the anion to the face of the π-system, and the arrangements of thiocyanate followed the areas of the most positive potentials on the surfaces of the π-acceptors. The stabilities of the complexes increased with the π-acceptor strength (reflected by their reduction potentials), and were essentially independent of the magnitudes of the maximum electrostatic potentials on their surfaces. The complexes showed intense absorption bands in the UV-Vis range, and the energies of these bands were correlated with the difference of the redox potentials of the anions and π-acceptors. Such features, as well as results of atoms-in-molecules and non-covalent index analyses suggested that besides electrostatics, molecular orbital interactions play a substantial role in the formation of these complexes. The unified trends in variations of the characteristics of the complexes between thiocyanate and a variety of π-acceptors point to their common nature. To embrace diversity and uniformity of the anion-π associates, we suggest (following the halogen bond's definition) that anion-π bonding occurs when there is evidence of a net attraction between the anions and the face of the electrophilic π-system.

Tuning surface strain is a powerful strategy for tailoring the reactivity of metal catalysts. Traditionally, surface strain is imposed by external stress from a heterogeneous substrate, but the effect is often obscured by interfacial reconstructions and nanocatalyst geometries. Here, we report on a strategy to resolve these problems by exploiting intrinsic surface stresses in two-dimensional transition metal nanosheets. Density functional theory calculations indicate that attractive interactions between surface atoms lead to tensile surface stresses that exert a pressure on the order of 105 atmospheres on the surface atoms and impart up to 10% compressive strain, with the exact magnitude inversely proportional to the nanosheet thickness. Atomic-level control of thickness thus enables generation and fine-tuning of intrinsic strain to optimize catalytic reactivity, which was confirmed experimentally on Pd(110) nanosheets for the oxygen reduction and hydrogen evolution reactions, with activity enhancements that were more than an order of magnitude greater than those of their nanoparticle counterparts.

The elucidation of governing principles of catalyst activity from fundamental research on well-defined model systems, combined with the advances in controlled synthesis of the transition metal nanocatalysts, have led to the rational design and development of advanced nanocatalysts. One principle is to tune surface strain and thereby modulate the surface electronic structure and catalytic reactivity. This approach has been employed to identify transition metal and (hydroxy)oxide catalysts for energy-relevant electrocatalytic reactions. For oxygen reduction reaction on platinum as an example, previous studies have indicated that a 1% compressive strain may improve the activity over 300%. While strain tuning of catalysts is very promising for activity enhancement, two challenges continue to limit the performance of these materials. First, the effect of strain on high-surface-area nanoparticle catalysts is often convoluted with nanoparticle shape and by the presence of undercoordinated defects, while model catalysts, where high specific activities is achievable by strain tuning, have prohibitively small mass activities and are therefore of limited practical interest. Thus, there is an urgent need to develop strain tuning strategies for nanomaterials that preserve the structural simplicity and high specific activity of single crystals while simultaneously possessing high surface areas and mass activities. In addition, strain in catalyst overlayers is often imposed by heterosubstrates, which may lead to interfacial reconstructions and strain release. In this talk, in combination DFT calculations, electrochemical tests and TEM characterization, we will present a strategy to resolve these problems through self-driven strain tuning in transition metal nanocrystals with the characteristics of low-index single-crystal surfaces. As we demonstrate for the oxygen reduction reaction in alkaline fuel cells, this relationship could be exploited to enhance reaction rates by orders of magnitude in comparison with nanoparticle catalysts, suggesting that this self-driven tuning mechanism in transition metal nanocrystals can be a powerful strategy for the design and development of advanced catalytic materials.

The atomic simulation environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simulations. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple 'for-loop' construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.