Using density functional theory, we studied the bulk and surface properties of Li and Na electrodes on an atomistic level. To get a better understanding of the initial stages of surface growth phenomena (and thus dendrite formation), various self‐diffusion mechanisms were studied. For this purpose, dedicated diffusion pathways on the surfaces of Na and Li were investigated within the terrace‐step‐kink (TSK) model utilizing nudged elastic band calculations. We were able to prove that the mere investigation of terrace self‐diffusion on the respective low‐index surfaces does not provide a possible descriptor for dendritic growth. Finally, we provide an initial view of the surface growth behavior of both alkali metals as well as provide a basis for experimental investigations and theoretical long‐scale kinetic Monte Carlo simulations.

Rechargeable magnesium-ion batteries (MIBs) are a promising alternative to commercial lithium-ion batteries (LIBs). They are safer to handle, environmentally more friendly, and provide a five-time higher volumetric capacity (3832 mAh cm À 3 ) than commercialized LIBs. However, the formation of a passivation layer on metallic Mg electrodes is still a major challenge towards their commercialization. Using density functional theory (DFT), the atomistic properties of metallic magnesium, mainly well-selected self-diffusion processes on perfect and imperfect Mg surfaces were investigated to better understand the initial surface growth phenomena. Subsequently, rate constants and activation temperatures of crucial diffusion processes on Mg(0001) and Mg(101 1) were determined, providing preliminary insights into the surface kinetics of metallic Mg electrodes. The obtained DFT results provide a data set for parametrizing a force field for metallic Mg or performing kinetic Monte-Carlo simulations.

In this work, we demonstrate the superior exploration capabilities of the population-based methods over the sequential one-parameter parabolic interpolation (SOPPI) approach to optimise ReaxFF force field parameters. Evolutionary algorithms (EAs) are heuristic-based approaches using a population of concurrent models in the search space to evolve towards the global best through stochastic operations. The parallelisation of EAs scales almost linearly, and no differentiable objective function is required. These methods were tested for their search performance and convergence behaviour on different multi-dimensional, multimodal benchmark functions. The developed KVIK (Icelandic for: dynamic, in motion) optimisation framework features an extended training 1routine designed to parameterise solid-state systems efficiently. The optimisation routine was applied to train a reactive force field potential for metallic lithium and sodium and their interaction parameters. The KVIK-optimised ReaxFF potential function parameter set reproduces relative energy results from the density functional theory (DFT) reference data set within the standard deviation range established using the error estimation routine provided by the BEEF-vdW density functional. Finally, thermodynamically and kinetically driven surface growth phenomena on metallic Li- and Na-electrodes were investigated using coupled ReaxFF/Monte Carlo (MC) approaches.

Lithium‐ion batteries pose certain drawbacks and alternatives are highly demanded. Requirements such as low corrosiveness, electrochemical stability and suitable electrolytes can be met by magnesium‐ion batteries. Metalation of carbazole with Mg in THF in the presence of ethyl bromide yields the sparingly soluble Hauser base [(thf)3Mg(Carb)Br] (1) which shows a Schlenk‐type equilibrium with formation of [(thf)3Mg(Carb)2] and [(thf)4MgBr2]. A THF solution of 1 shows a low over‐potential and a good cyclability of electrodeposition/‐stripping of Mg on a Cu current collector. An improved performance is achieved with the turbo‐Hauser bases [(thf)(Carb)Mg(μ‐Br/X)2Li(thf)2] (X=Br (2) and Cl (3)) which show a significantly higher solubility in ethereal solvents. The THF solvation energies increase from (thf)xMgBr2 over (thf)xMg(Carb)Br to (thf)xMg(Carb)2 for an equal number x of ligated THF molecules.

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