Order parameter and connectivity topology analysis of crystalline ceramics for nuclear waste immobilization

Abstract

We apply bond order and topological methods to the problem of analysing the results of radiation damage cascade simulations in ceramics. Both modified Steinhardt local order and connectivity topology analysis techniques provide results that are both translationally and rotationally invariant and which do not rely on a particular choice of a reference structure. We illustrate the methods with new analyses of molecular dynamics simulations of single cascades in the pyrochlores Gd2Ti2O7 and Gd2Zr2O7 similar to those reported previously (Todorov et al 2006 J. Phys Condes. Mat. 18 2217).Results from the Steinhardt and topology analyses are consistent, while often providing complementary information, since the Steinhardt parameters are sensitive to changes in angular arrangement even when the overall topological connectivity is fixed. During the highly non-equilibrium conditions at the start of the cascade, both techniques reveal significant localised transient structural changes and variation in the cationconnectivity. After a few picoseconds, the connectivity is largely fixed, while the order parameters continue to change. In the zirconate there is a shift to the anion disordered system while in the titanate there is substantial reversion and healing back to the parent pyrochlore structure.3

Thermal annealing of natural, radiation-damaged pyrochlore

“…The local structure of synthetic pyrochlore-type materials have been extensively studied by Raman spectroscopy and the observed Raman signals could be assigned to certain phonon modes on the basis of model calculations [6,7,10,45,48,49,52,[58][59][60][61][62][63][64][65][66][67][68][69][70][71].…”

Growth of nano-domains in Gd–CeO2 mixtures: hybrid Monte Carlo simulations

“…37), to characterise non-stoichiometry at interfaces 38 and very recently the disorder induced by radiation damage and subsequent healing in ceramics. 39 These parameters are translationally and rotationally invariant, i.e., independent of the reference frame specifying the crystal structure.…”

DeepIce: A Deep Neural Network Approach To Identify Ice and Water Molecules

“…Identifying the local arrangements of molecules within condensed phases is a key task in computer simulations aimed at studying phenomena such as phase transitions, [1][2][3][4][5] nucleation, [6][7][8] crystal growth 9,10 and defect formation. 11,12 Ice surfaces, which strongly influence the macroscopic properties of ice, are an interesting example of multi-phase systems. 13 Over a significant temperature range, ice surfaces premelt and develop a quasi-liquid layer (QLL) which mediates crystal growth and chemical reactions.…”