Structure prediction of nanoclusters; a direct or a pre-screened search on the DFT energy landscape?

The atomic structure of inorganic nanoclusters obtained via a search for low lying minima on energy landscapes, or hypersurfaces, is reported for inorganic binary compounds: zinc oxide (ZnO)n, magnesium oxide (MgO)n, cadmium selenide (CdSe)n, and potassium fluoride (KF)n, where n = 1-12 formula units. The computational cost of each search is dominated by the effort to evaluate each sample point on the energy landscape and the number of required sample points. The effect of changing the balance between these two factors on the success of the search is investigated. The choice of sample points will also affect the number of required data points and therefore the efficiency of the search. Monte Carlo based global optimisation routines (evolutionary and stochastic quenching algorithms) within a new software package, viz. Knowledge Led Master Code (KLMC), are employed to search both directly and after pre-screening on the DFT energy landscape. Pre-screening includes structural relaxation to minimise a cheaper energy function - based on interatomic potentials - and is found to improve significantly the search efficiency, and typically reduces the number of DFT calculations required to locate the local minima by more than an order of magnitude. Although the choice of functional form is important, the approach is robust to small changes to the interatomic potential parameters. The computational cost of initial DFT calculations of each structure is reduced by employing Gaussian smearing to the electronic energy levels. Larger (KF)n nanoclusters are predicted to form cuboid cuts from the rock-salt phase, but also share many structural motifs with (MgO)n for smaller clusters. The transition from 2D rings to 3D (bubble, or fullerene-like) structures occur at a larger cluster size for (ZnO)n and (CdSe)n. Differences between the HOMO and LUMO energies, for all the compounds apart from KF, are in the visible region of the optical spectrum (2-3 eV); KF lies deep in the UV region at 5 eV and shows little variation. Extrapolating the electron affinities found for the clusters with respect to size results in the qualitatively correct work functions for the respective bulk materials

Thermodynamically accessible titanium clusters TiN, N = 2-32

We have performed a genetic algorithm search on the tight-binding interatomic potential energy surface (PES) for small TiN (N = 2-32) clusters. The low energy candidate clusters were further refined using density functional theory (DFT) calculations with the PBEsol exchange-correlation functional and evaluated with the PBEsol0 hybrid functional. The resulting clusters were analysed in terms of their structural features, growth mechanism and surface area. The results suggest a growth mechanism that is based on forming coordination centres by interpenetrating icosahedra, icositetrahedra and Frank-Kasper polyhedra. We identify centres of coordination, which act as centres of bulk nucleation in medium sized clusters and determine the morphological features of the cluster

Double bubbles: a new structural motif for enhanced electron-hole separation in solids

Electron-hole separation for novel composite systems comprised of secondary building units formed from different compounds is investigated with the aim of finding suitable materials for photocatalysis. Pure and mixed SOD and LTA superlattices of (ZnO)12 and (GaN)12, single-shell bubbles are constructed as well as core@shell single component frameworks composed of larger (ZnO)48 and (GaN)48 bubbles with each containing one smaller bubble. Enthalpies of formation for all systems are comparable with fullerenes. Hole and electron separation is achieved most efficiently by the edge sharing framework composed of (GaN)12@(ZnO)48 double bubbles, with the hole localised on the nitrogen within the smaller bubbles and the excited electron on zinc within the larger cages

On the Hardness of Energy Minimisation for Crystal Structure Prediction

Crystal Structure Prediction (csp) is one of the central and most challenging problems in materials science and computational chemistry. In csp, the goal is to find a configuration of ions in 3D space that yields the lowest potential energy. Finding an efficient procedure to solve this complex optimisation question is a well known open problem. Due to the exponentially large search space, the problem has been referred in several materials-science papers as "NP-Hard and very challenging" without a formal proof. This paper fills a gap in the literature providing the first set of formally proven NP-Hardness results for a variant of csp with various realistic constraints. In particular, we focus on the problem of removal: the goal is to find a substructure with minimal potential energy, by removing a subset of the ions. Our main contributions are NP-Hardness results for the csp removal problem, new embeddings of combinatorial graph problems into geometrical settings, and a more systematic exploration of the energy function to reveal the complexity of csp. In a wider context, our results contribute to the analysis of computational problems for weighted graphs embedded into the three-dimensional Euclidean space.Comment: Short version published in SOFSEM 2020, full version to be published in Fundamenta Informatica

The search for the ideal biocatalyst

While the use of enzymes as biocatalysts to assist in the industrial manufacture of fine chemicals and pharmaceuticals has enormous potential, application is frequently limited by evolution-led catalyst traits. The advent of designer biocatalysts, produced by informed selection and mutation through recombinant DNA technology, enables production of process-compatible enzymes. However, to fully realize the potential of designer enzymes in industrial applications, it will be necessary to tailor catalyst properties so that they are optimal not only for a given reaction but also in the context of the industrial process in which the enzyme is applied

Head to head: the case for fighting behaviour in Megaloceros giganteus using finite element analysis

The largest antlers of any known deer species belonged to the extinct giant deer Megaloceros giganteus. It has been argued that their antlers were too large for use in fighting, instead being used only in ritualised displays to attract mates. Here we used finite element analysis (FEA) to test whether the antlers of M. giganteus could have withstood forces generated during fighting. We compared the mechanical performance of antlers in M. giganteus with three extant deer species: red deer (Cervus elaphus), fallow deer (Dama dama), and moose (Alces alces). Von Mises stress results suggest that M. giganteus was capable of withstanding some fighting loads, provided that their antlers interlocked proximally, and that it’s antlers were best-adapted for withstanding loads from twisting rather than pushing actions, as are other deer with palmate antlers. We conclude that fighting in M. giganteus was likely more constrained and predictable than in extant deer