Probe the Dynamic Adsorption and Phase Transition of Underpotential Deposition Processes at Electrode-Electrolyte Interfaces.

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 (√3 × âˆš3)R30° copper UPD adlayer with coadsorbed SO42- while changing sample potential (ES) toward the redox Pa2/Pc2 peaks, which are associated with the phase transition between the Cu UPD (√3 × âˆš3)R30° 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 (√3 × âˆš3)R30° 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.

GPAW: An open Python package for electronic structure calculations.

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 the 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, 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 for graphics processing unit (GPU) acceleration has been achieved with minor modifications to the GPAW code thanks to the CuPy library. We end the review with an outlook, describing some future plans for GPAW.

Halogen Bonding Between Anions: Association of Anion Radicals of Tetraiodo-p-benzoquinone with Iodide Anions.

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.

Transferable Water Potentials Using Equivariant Neural Networks.

Machine learning interatomic potentials (MLIPs) have emerged as a technique that promises quantum theory accuracy for reduced cost. It has been proposed [J. Chem. Phys. 2023, 158, 084111] that MLIPs trained on solely liquid water data cannot accurately transfer to the vapor-liquid equilibrium while recovering the many-body decomposition (MBD) analysis of gas-phase water clusters. This suggests that MLIPs do not directly learn the physically correct interactions of water molecules, limiting transferability. In this work, we show that MLIPs using equivariant architecture and trained on 3200 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 MBD analysis of gas-phase water cluster up to six-body interactions, and the relative energy and the vibrational density of states of ice phases. We show that potentials developed using equivariant MLIPs allow transferability for arbitrary phases of water that remain stable in nanosecond long simulations.

Diversity and uniformity in anion-π complexes of thiocyanate with aromatic, olefinic and quinoidal π-acceptors.

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.

Tunable intrinsic strain in two-dimensional transition metal electrocatalysts.

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 atomic simulation environment-a Python library for working with atoms.

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.