Interface between graphene and liquid Cu from molecular dynamics simulations

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 {\AA} 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 atomic-scale 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.Comment: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in J. Chem. Phys. 153, 074702 (2020) and may be found at https://doi.org/10.1063/5.002012

Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects

We first briefly report on the status and recent achievements of the ELPA-AEO (Eigenvalue Solvers for Petaflop Applications - Algorithmic Extensions and Optimizations) and ESSEX II (Equipping Sparse Solvers for Exascale) projects. In both collaboratory efforts, scientists from the application areas, mathematicians, and computer scientists work together to develop and make available efficient highly parallel methods for the solution of eigenvalue problems. Then we focus on a topic addressed in both projects, the use of mixed precision computations to enhance efficiency. We give a more detailed description of our approaches for benefiting from either lower or higher precision in three selected contexts and of the results thus obtained

Simulation of static and dynamic magnetic resonance parameters for solid mixed conductors

A detailed understanding of the atomistic processes within a lithium ion battery is expected to facilitate the progress of energy storage technology, which is a key element for the exit from nuclear and fossil--fuel energy. Nuclear magnetic resonance (NMR) spectroscopy can provide information about local electronic structure and dynamic properties of battery materials by addressing the nuclear spins of lithium (Li). Support by independent $\textit{ab initio}$ simulations is indispensable to meet the challenges presented by state--of--the--art electrode materials in NMR spectroscopy. In this thesis, the initial steps towards a novel, theoretical multi--scale method for retracing Li NMR experiments on battery materials are presented. First, the theoretical concepts of calculating NMR parameters are summarized. Then, the accuracy of NMR parameter simulations for Li and their dependence on different criteria are benchmarked. On the basis of the high--capacitance, disordered electrode material Li$_4$Ti$_5$O$_{12}$ (LTO), an approach for clustering the NMR quantities according to the local, crystallographic structure is established, reducing the number of $\textit{ab initio}$ NMR calculations. Theoretical sampling of the LTO configuration space demonstrates that the customary one--to--one assignment of experimental observables to crystallographic positions is inaccurate and misleading for complex materials. Finally, a kinetic Monte--Carlo (kMC) model, which simulates the atomistic dynamics, is combined with the NMR autocorrelation function (ACF), which provides the effective observables for spin--alignment echo (SAE) experiments. The simulated, static Li NMR parameters enter the kMC model together with atomic mobility parameters. The kMC sampling of the NMR ACF finally yields observables, which compared to experimental findings confirm the hypothesis of two domains of mobility on separate length and time scales in LTO. Additionally, a theoretical pre--screening approach for organic pH--marker molecules in aqueous solution is outlined. The pre--screening approach is demonstrated for biomedical applications, but might be transferred to energy research applications to study local reaction conditions of transient electrochemical processes. The various results demonstrate the synergy of combining theoretical simulations with NMR experiments. Together theory and experiment amplify the gained knowledge and make additional insights into the basic processes in a battery available