Adama Tandia

Origin of dynamical heterogeneities in calcium aluminosilicate liquids

We investigate the heterogeneous dynamics of calcium aluminosilicate liquids across both the peraluminous and peralkaline regimes. Using the isoconfigurational ensemble method we find a clear correlation between dynamical heterogeneities and concentration fluctuations. Regions of high dynamic propensity have higher concentrations of both calcium and aluminum, whereas low propensity regions are silica rich. The isoconfigurational ensemble is found to be a powerful tool for studying the origin of heterogeneous dynamics of industrially relevant glass-forming liquids.

Crack nucleation criterion and its application to impact indentation in glasses

Molecular dynamics (MD) simulations are used to directly observe nucleation of median cracks in oxide glasses under indentation. Indenters with sharp angles can nucleate median cracks in samples with no pre-existing flaws, while indenters with larger indenter angles cannot. Increasing the tip radius increases the critical load for nucleation of the median crack. Based upon an independent set of simulations under homogeneous loading, the fracture criterion in the domain of the principal stresses is constructed. The fracture criterion, or “fracture locus”, can quantitatively explain the observed effects of indenter angle and indenter tip radius on median crack nucleation. Our simulations suggest that beyond the maximum principal stress, plasticity and multi-axial stresses should also be considered for crack nucleation under indentation, even for brittle glassy systems.

Competing Indentation Deformation Mechanisms in Glass Using Different Strengthening Methods

Chemical strengthening via ion exchange, thermal tempering, and lamination are proven techniques for strengthening of oxide glasses. For each of these techniques, the strengthening mechanism is conventionally ascribed to the linear superposition of the compressive stress profile on the glass surface. However, in this work we use molecular dynamics simulations to reveal the underlying indentation deformation mechanism beyond the simple linear superposition of compressive and indentation stresses. In particular, the plastic zone can be dramatically different from the commonly assumed hemispherical shape, which leads to a completely different stress field and resulting crack system. We show that the indentation-induced fracture is controlled by two competing mechanisms: the compressive stress itself and a potential reduction in free volume that can increase the driving force for crack formation. Chemical strengthening via ion exchange tends to escalate the competition between these two effects, while thermal tempering tends to reduce it. Lamination of glasses with differential thermal expansion falls in between. The crack system also depends on the indenter geometry and the loading stage, i.e., loading vs. after unloading. It is observed that combining thermal tempering or high free volume content with ion exchange or lamination can impart a relatively high compressive stress and reduce the driving force for crack formation. Therefore, such a combined approach might offer the best overall crack resistance for oxide glasses.

Predicting the dissolution kinetics of silicate glasses using machine learning

Abstract Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, even for untrained data, thanks to its inherent ability to handle non-linear data. We further note that the predictive ability of simpler methods, such as linear regression, could be improved using additional physics-based constraints. Such methods, called as physics-informed machine learning can be used to extrapolate the behavior of untrained compositions as well. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties.