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 attention, 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 statistical 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.

Computational screening based on first-principles microkinetic modeling has evolved into a widespread tool for catalyst discovery. Efficiently exploiting various scaling relations, this approach draws its predictive character from reliable adsorption energies, typically calculated with density-functional theory (DFT). In prevalent screening approaches, the concomitant computational costs are kept tractable through the use of reductionist microkinetic models that only resolve a minimalistic amount of active site motifs at the catalyst surface. Here, we scrutinize this common practice by systematically comparing the screening predictions for the CO methanation reaction when using microkinetic models that resolve an increasing amount of sites, up to the full consideration of all high-symmetry sites at stepped transition metal (TM) and binary TM alloy catalysts. Apart from generally overestimating the catalytic activity, the simplified models fail to identify a most promising class of layered bimetallic alloys as their insufficient representation of the catalyst surface does not allow them to correctly capture the rate-determining step. Only the full microkinetic model provides this proper mechanistic basis for the screening. The excessive amount of predictive-quality adsorption energetics required for this model is obtained from a compressed sensing descriptor that once trained readily provides these data for a new material from a single DFT calculation of the clean surface. With the current methodological advances in areas such as compressed sensing and machine learning, and the concurrent availability of cheap adsorption energetics for a wide range of possible catalyst materials, there is thus no reason to continue to use simplistic microkinetic models in computational catalyst screening.

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 Å 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 atomicscale 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.

Predictive-quality computational modeling of heterogeneously catalyzed reactions has emerged as an important tool for the analysis and assessment of activity and activity trends. In contrast, more subtle selectivities and selectivity trends still pose a significant challenge to prevalent microkinetic modeling approaches that typically employ a mean-field approximation (MFA). Here, we focus on CO hydrogenation on Rh catalysts with the possible products methane, acetaldehyde, ethanol, and water. This reaction has already been subjected to a number of experimental and theoretical studies with conflicting views on the factors controlling activity and selectivity toward the more valuable higher oxygenates. Using accelerated first-principles kinetic Monte Carlo simulations and explicitly and systematically accounting for adsorbate–adsorbate interactions through a cluster expansion approach, we model the reaction on the low-index Rh(111) and stepped Rh(211) surfaces. We find that the Rh(111) facet is selective toward methane, while the Rh(211) facet exhibits a similar selectivity toward methane and acetaldehyde. This is consistent with the experimental selectivity observed for larger, predominantly (111)-exposing Rh nanoparticles and resolves the discrepancy with earlier first-principles MFA microkinetic work that found the Rh(111) facet to be selective toward acetaldehyde. While the latter work tried to approximately account for lateral interactions through coverage-dependent rate expressions, our analysis demonstrates that this fails to sufficiently capture concomitant correlations among the adsorbed reaction intermediates that crucially determine the overall selectivity.