Global optimisation of hydroxylated silica clusters: A cascade Monte Carlo Basin Hopping approach

Abstract

We report on a global optimisation study of hydroxylated silica nanoclusters (SiO 2 ) M ·(H 2 O) N with sizes M = 6, 8, 10 12, and for each size with a variable number of incorporated water molecules (N = 1, 2, 3…). Due to the high structural complexity of these systems and the associated ruggedness of the underlying potential energy landscape, we propose a "cascade" global optimisation approach.Specifically, we use Monte Carlo Basin Hopping (MCBH) where for each step we employ two energy minimisations with: (i) a few-term simple but computationally efficient interatomic potential (IP) which does not distinguish between H-bonded conformational isomers, and then (ii) a more sophisticated IP which accounts for polarisation and H-bonding. Final energies from the MCBH search are then refined with optimisations using density functional theory. The reliability of our approach is first established via comparison with previously reported results for the (SiO 2 ) 8 ·(H 2 O) N case, and then applied to the M = 6, 10 and 12 systems. For all systems studied our results follow the trend in hydroxylation energy versus N, whereby the energy gain with hydroxylation is found to level off at a point where the average tetrahedral distortion of the SiO 4 centres is minimised. This optimal hydroxylation point is further found to follow an inverse power law with increasing cluster size (M) with an exponent close to -2/3, further confirming work in previous studies for other cluster sizes.

Does Processing or Formation of Water Ice Mantles Affect the Capacity of Nanosilicates to Be the Source of Anomalous Microwave Emission?

“…In addition, we also allowed that 0.5% of the steps attempt an Mg ↔ Si cation exchange move assist in this exploration. To run the MCBH calculations, we employed a previously developed cascade MCBH code (Cuko et al, 2017), which uses the General Utility Lattice Package (GULP) (Gale and Rohl, 2003) to locally optimize the cluster structures at each step. To avoid problems with the local optimization of distorted structures containing polarized ions, we preoptimize each cluster structure with an IP with polarizable shells removed before optimizing using the IP with shells.…”

Properties of hydrated TiO2and SiO2nanoclusters: dependence on size, temperature and water vapour pressure

“…Our MCBH searches were also further extended by data mining from hydrated silica nanoclusters from our previous works. 39,40,41 From each MCBH run a selection of isomers were optimised using progressively more accurate methods. Firstly, the 200-500 lowest energy cluster isomers optimised using FFTiOH* and FFTiOH*-mod during each MCBH run were first post-optimised optimized with FFTiOH.…”

“…The global optimized structures show a huge range of variability with respect to size and chemical composition, yet, for small sizes they are more stable than crystal cuts of the same size. 33,34 Herein, 4 following our previous theoretical work on anhydrous SiO2, 35,36,37 TiO2 33 , and TiO2-SiO2 38 nanoclusters and hydroxylated SiO2 39,40,41 nanoclusters, we use global optimisation to provide detailed bottom-up insights into the structures and stabilities of (TiO2)M(H2O)N nanoclusters with sizes from M = 4 -16 and N/M ratios of ≥ 0.5 (i.e. up to 72 atoms).…”

Thermodynamically accessible titanium clusters TiN, N = 2–32

“…Alternative global optimisation techniques, typically based on either a GA or a Monte Carlo Basin Hopping scheme, have been applied to predicting the structure of metallic clusters or their alloys or generic atomistic energy landscapes. [43][44][45][46][47] First, the GA module was employed to perform a search on the semiclassical interatomic PES, using the GULP code 48,49 for energy and force evaluations, and local geometry optimisations. Geometry optimisations were performed using the BFGS minimiser followed by the Rational Function Optimisation method to ensure that the system converges to a stable stationary point.…”