The comprehension of the nonlinear effects provided by mixed alkali effect (MAE) in oxide glasses is useful to optimize glass compositions to achieve specific properties that depend on the mobility of ions, such as the chemical durability, glass transition temperature, viscosity and ionic conductivity. Although molecular dynamics (MD) simulations have already been applied to investigate the MAE on silicates, less effort has been devoted to study such phenomenon in mixed alkali aluminosilicate glasses where alkali cations can act both as modifiers, forming non-bridging oxygens and percolation channels, and as charge compensator of the Alo 4 − units present in the network. Moreover, the ionic conductivity has not been computed yet; thus, the accuracy of the atomistic simulations in reproducing the MAE on the property is still open to question. In this work, we have validated five major interatomic potentials for the classical MD simulations by modelling the structure, density, glass transition temperature and ionic conductivity for three aluminosilicate glasses, (25 − x)Na 2 o − x(K 2 O) − 10(Al 2 o 3) − 65(SiO 2) (x = 0, 12.5, 25). It was observed that only the core-shell (CS) polarizable force field well reproduces the experimentally measured MAe on T g and the ionic conductivity as well as the higher conductivity of single sodium aluminosilicate glass at low temperature and the higher conductivity of single potassium aluminosilicate glass at high temperature. The MAE is related to the suppression of jump events of the alkaline ions between dissimilar sites in the percolation channels consisting of both sodium and potassium ions as in the case of alkaline silicates. The superior reproducibility of the CS potential is originated from the larger and the flexible ring structures due to the smaller Si-O-Si inter-tetrahedra angle, creating appropriate percolation channels for ion conductivity. We also report detailed assessments for using the potential models including the CS potential for investigating MAE on aluminosilicates. Ionic conductivity in inorganic glasses is gaining a huge interest for the enormous number of applications that such materials are having in a number of major technological developments in the domains of energy conversion and storage (solid electrolytes in battery and fuel cells) or in the environmental monitoring (solid state ionic membranes for sensors) 1. Among the various ways to control ionic diffusion and thus ionic conductivity a possibility is that to exploit the so-called Mixed Alkali Effect (MAE) or more in general the Mixed Ion Effect (MIE) 2,3. This effect refers to a large non-linear deviation in glass properties observed when an alkali cation (or more in general a mobile ion) is gradually replaced by another alkali cation. The ionic conductivity of glasses exhibiting the MAE shows a deep minimum when half of the alkaline ions of type A are replaced by type B ions, meaning that cation mobility progressively reduces up to a substitution ratio equal to unity. The minimum is more pron...

We have investigated the ability of two modular phyllosilicates (palygorskite and sepiolite) to store CO2 molecules inside their structural channels by means of classical molecular dynamics. Several models containing an increasing supercritical-CO2/H2O ratio into the phyllosilicate channels have been built and the structural and dynamic properties of carbon dioxide and water molecules investigated in detail. We found that both clay minerals can achieve this goal, with sepiolite being able to store more carbon dioxide molecules (and more stably) than palygorskite, due to the larger channels of the former. Interestingly, with the increase of CO2 molecules inside the minerals, the diffusivity of both water and carbon dioxide drastically decreases and carbon dioxide molecules tend to arrange themselves in an ordered pattern. (Figure Presented)

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Unraveling detailed mechanism of crystal nucleation from amorphous materials is challenging for both experimental and theoretical approaches. In this study, we have examined two methods to understand the initial stage of crystal precipitation from lithium disilicate glasses using molecular dynamics simulations. One of the methods is a modified exploring method to find structurally similar crystalline clusters in the glass models, enabling us to find three different embryos, such as Li2Si2O5 (LS2), Li2SiO3 (LS) and Li3PO4 (LP), in the 33Li2O·66SiO2·1P2O5 glass (LS2P1), in which P2O5 is added as a nucleating agent. Interestingly, LS2 and LP crystals were found inside the LS2P1 glass while LS crystal appeared on the glass surface, which agrees with experimental observations. The other method is free energy calculation using a subnano-scale spherical crystal embedded in the glass model. This method, which we called Free-Energy Seeding Method (FESM), allows us to evaluate free energy change as a function of crystal radius and to identify critical size of the crystal precipitation. The free energy profiles for LS and LS2 crystal nuclei in the LS2 glass models possess maximum energy at a critical radius as expected by classical nucleation theory. Furthermore, the critical radius and the energy barrier height agree well with recent experimental investigation, proving the applicability of this method to design glass–ceramics by atomistic modeling.

Metadynamics is a useful technique to study rare events such as crystallization. It has been only recently applied to study nucleation and crystallization in glass-forming liquids such as silicates but the optimal set of parameters to drive crystallization and obtaining converged Free Energy Surfaces is still unexplored. <p>In this work, we systematically investigated the effects of the simulation conditions to efficiently study the thermodynamics and mechanism of crystallization in highly viscous systems. As a prototype system, we used fused silica, which easily crystallizes to β-cristobalite through MetaD simulations, owing to its simple microstructure. We investigated the influence of the height, width, and bias factor used to define the biasing Gaussian potential, as well as the effects of the temperature and system size on the results. Among these parameters, the bias factor and temperature seem to be most effective to sample the free energy landscape of melt to crystal transition and reach convergence more quickly. We also demonstrate that the temperature rescaling from T &gt; Tm is a reliable approach to recover free energy surfaces below Tm, provided that the temperature gap is below 600 K and the configurational space has been properly sampled. Finally, albeit a complete crystallization is hard to achieve with large simulation boxes, these can be reliably and effectively exploited to study the first stages of nucleation.