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Abstract Future carbon management strategies require storage in elemental form, achievable through a sequence of CO2 hydrogenation reactions. Hydrogen is recycled from molecular intermediates by dehydrogenation, and side product acetylene selectively hydrogenated to ethylene. Existing Pd alloy catalysts for gas purification underperform in concentrated feeds, necessitating novel concepts. Atomistic simulations unveil superior selectivity of Pd:C solid solutions that optimize chemisorption energies and preclude sub-surface hydrides, verified here with model thin films. Multiple design criteria deduced from conventional catalysts facilitate synthesizing a self-repairing Pd:C system of a laterally condensed catalyst (LCC). A Pd layer prepared on a designated SiO2 buffer layer enables control of reactive interface, sub-surface volume and extended functional interface towards the buffer. Function and metric are supervised by operando micro-spectroscopy. This catalyst design shows, ethylene productivity >1 kmolC2H4/gPd/hour is reproducibly achieved and benchmarked against known catalysts. Photovoltaics deposition technologies enable scalability on real-world substrates saving active metal. A design-of-experiment approach demonstrates the improvement potential of the LCC approach.

Abstract The interaction of borazine with metal supports and the concomitant surface chemistry play important roles in the synthesis of hexagonal boron nitride and the assembly of BN‐doped carbon nanostructures, thus making adsorbed borazine an intriguing model system. Herein, the first real space characterization of individual borazine molecules and highly ordered borazine self‐assemblies on solid supports, combining scanning tunneling microscopy (STM), scanning tunneling spectroscopy, X‐ray photoelectron spectroscopy, and complementary density functional theory modeling is reported. Specifically, a weak, nondissociative adsorption of borazine with the ring aligned in parallel to the surface plane is observed on Ag(111) upon low temperature deposition. Borazine is found to favor hollow adsorption sites, which guide the assembly of intricate borazine assemblies including a porous, chiral honeycomb‐like network, and dense‐packed monolayer films. Additionally, a modification of the borazine adsorption configuration by STM‐based manipulation is demonstrated. Dehydrogenation of individual molecules by voltages pulses yields an upright standing borazine fragment bound via B to Ag. This study thus provides a comprehensive, single‐molecule level characterization of borazine adsorption and surface chemistry on a characteristic coinage metal support and may serve as a reference for advanced low‐dimensional materials based on functionalized borazines or including BN units as dopants.

Abstract The adsorption energies of molecular adsorbates on catalyst surfaces are key descriptors in computational catalysis research. For the relatively large reaction intermediates frequently encountered, e.g., in syngas conversion, a multitude of possible binding motifs leads to complex potential energy surfaces (PES), however. This implies that finding the optimal structure is a difficult global optimization problem, which leads to significant uncertainty about the stability of many intermediates. To tackle this issue, we present a global optimization protocol for surface adsorbate geometries which trains a surrogate machine learning potential on-the-fly. The approach is applicable to arbitrary surface models and adsorbates and minimizes both human intervention and the number of required DFT calculations by iteratively updating the training set with configurations explored by the algorithm. We demonstrate the efficiency of this approach for a diverse set of adsorbates on the Rh(111) and (211) surfaces.

Propylene and propylene oxide are formed over boron nitride or SiO2 in the gas phase without yielding large amounts of CO2. Conversion at non-specific interfaces can thus be a successful strategy for the synthesis of oxidation-sensitive products.

Abstract Liquid metal catalysts have recently attracted attention for synthesizing high‐quality 2D materials facilitated via the catalysts’ perfectly smooth surface. However, the microscopic catalytic processes occurring at the surface are still largely unclear because liquid metals escape the accessibility of traditional experimental and computational surface science approaches. Hence, numerous controversies are found regarding different applications, with graphene (Gr) growth on liquid copper (Cu) as a prominent prototype. In this work, novel in situ and in silico techniques are employed to achieve an atomic‐level characterization of the graphene adsorption height above liquid Cu, reaching quantitative agreement within 0.1 Å between experiment and theory. The results are obtained via in situ synchrotron X‐ray reflectivity (XRR) measurements over wide‐range q‐vectors and large‐scale molecular dynamics simulations based on efficient machine‐learning (ML) potentials trained to first‐principles density functional theory (DFT) data. The computational insight is demonstrated to be robust against inherent DFT errors and reveals the nature of graphene binding to be highly comparable at liquid Cu and solid Cu(111). Transporting the predictive first‐principles quality via ML potentials to the scales required for liquid metal catalysis thus provides a powerful approach to reach microscopic understanding, analogous to the established computational approaches for catalysis at solid surfaces.

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.

Semilocal density functionals, while computationally efficient, do not account for non-local correlation. Here, the authors propose a machine-learning approach to DFT that leads to non-local and transferable functionals applicable to non-covalent, ionic and covalent interactions across system of different sizes.

Application of machine-learning approaches to exploring chemical reaction networks is challenging due to need of including open-shell reaction intermediates. Here the authors introduce a density functional theory database of closed and open-shell molecules for machine-learning predictions of reaction energies.

While great effort has been focused on bulk material design for high-performance All Solid-State Batteries (ASSBs), solid-solid interfaces, which typically extend over a nanometer regime, have been identified to severely impact cell performance. Major challenges are Li dendrite penetration along the grain boundary network of the Solid-State Electrolyte (SSE) and reductive decomposition at the electrolyte/electrode interface. A naturally forming nanoscale complexion encapsulating ceramic Li1+xAlxTi2−x(PO4)3 (LATP) SSE grains has been shown to serve as a thin protective layer against such degradation mechanisms. To further exploit this feature, we study the interfacial doping of divalent Mg2+ into LATP grain boundaries. Molecular Dynamics simulations for a realistic atomistic model of the grain boundary reveal Mg2+ to be an eligible dopant candidate as it rarely passes through the complexion and thus does not degrade the bulk electrolyte performance. Tuning the interphase stoichiometry promotes the suppression of reductive degradation mechanisms by lowering the Ti4+ content while simultaneously increasing the local Li+ conductivity. The Mg2+ doping investigated in this work identifies a promising route towards active interfacial engineering at the nanoscale from a computational perspective.

The lithium thiophosphate (LPS) material class provides promising candidates for solid-state electrolytes (SSEs) in lithium ion batteries due to high lithium ion conductivities, non-critical elements, and low material cost. LPS materials are characterized by complex thiophosphate microchemistry and structural disorder influencing the material performance. To overcome the length and time scale restrictions of ab initio calculations to industrially applicable LPS materials, we develop a near-universal machine-learning interatomic potential for the LPS material class. The trained Gaussian Approximation Potential (GAP) can likewise describe crystal and glassy materials and different P-S connectivities PmSn. We apply the GAP surrogate model to probe lithium ion conductivity and the influence of thiophosphate subunits on the latter. The materials studied are crystals (modifications of Li3PS4 and Li7P3S11), and glasses of the xLi2S–(100 – x)P2S5 type (x = 67, 70 and 75). The obtained material properties are well aligned with experimental findings and we underscore the role of anion dynamics on lithium ion conductivity in glassy LPS. The GAP surrogate approach allows for a variety of extensions and transferability to other SSEs.

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.

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.

The metal-organic framework (MOF) MFU-4 l containing Co(II) centers and Cl ^− ligands has recently shown promising redox activity. Aiming for further improved MOF catalysts for oxidation processes employing molecular oxygen we present a density-functional theory (DFT) based computational screening approach to identify promising metal center and ligand combinations within the MFU-4 l structural family. Using the O _2 binding energy as a descriptor for the redox property, we show that relative energetic trends in this descriptor can reliably be obtained at the hybrid functional DFT level and using small cluster (scorpionate-type complex) models. Within this efficient computational protocol we screen a range of metal center/ligand combinations and identify several candidate systems that offer more exothermic O _2 binding than the original Co/Cl-based MFU-4 l framework.

Developing new methods, other than size and shape, for controlling the optoelectronic properties of semiconductor nanocrystals is a highly desired target. Here we demonstrate that the photoluminescence (PL) of silicon nanocrystals (SiNCs) can be tuned in the range 685–800 nm solely via surface functionalization with alkynyl(aryl) (phenylacetylene, 2-ethynylnaphthalene, 2-ethynyl-5-hexylthiophene) surface groups. Scanning tunneling microscopy/spectroscopy on single nanocrystals revealed the formation of new in-gap states adjacent to the conduction band edge of the functionalized SiNCs. PL red-shifts were attributed to emission through these in-gap states, which reduce the effective band gap for the electron–hole recombination process. The observed in-gap states can be associated with new interface states formed via (–Si–C≡C–) bonds in combination with conjugated molecules as indicated by ab initio calculations. In contrast to alkynyl(aryl)s, the formation of in-gap states and shifts in PL maximum of the SiNCs were not observed with aryl (phenyl, naphthalene, 2-hexylthiophene) and alkynyl (1-dodecyne) surface groups. These outcomes show that surface functionalization with alkynyl(aryl) molecules is a valuable tool to control the electronic structure and optical properties of SiNCs via tuneable interface states, which may enhance the performance of SiNCs in semiconductor devices. [ABSTRACT FROM AUTHOR]