Fracture Toughness of Silicate Glasses: Insights from Molecular Dynamics Simulations

Understanding, predicting and eventually improving the resistance to fracture of silicate materials is of primary importance to design new glasses that would be tougher, while retaining their transparency. However, the atomic mechanism of the fracture in amorphous silicate materials is still a topic of debate. In particular, there is some controversy about the existence of ductility at the nano-scale during the crack propagation. Here, we present simulations of the fracture of three archetypical silicate glasses using molecular dynamics. We show that the methodology that is used provide realistic values of fracture energy and toughness. In addition, the simulations clearly suggest that silicate glasses can show different degrees of ductility, depending on their composition.Comment: arXiv admin note: text overlap with arXiv:1410.291

Nature of Radiation-Induced Defects in Quartz

Although quartz ($\rm \alpha$-form) is a mineral used in numerous applications wherein radiation exposure is an issue, the nature of the atomistic defects formed during radiation-induced damage have not been fully clarified. Especially, the extent of oxygen vacancy formation is still debated, which is an issue of primary importance as optical techniques based on charged oxygen vacancies have been utilized to assess the level of radiation damage in quartz. In this paper, molecular dynamics (MD) simulations are applied to study the effects of ballistic impacts on the atomic network of quartz. We show that the defects that are formed mainly consist of over-coordinated Si and O, as well as Si--O connectivity defects, e.g., small Si--O rings and edge-sharing Si tetrahedra. Oxygen vacancies, on the contrary, are found in relatively low abundance, suggesting that characterizations based on $E^{\prime}$ centers do not adequately capture radiation-induced structural damage in quartz. Finally, we evaluate the dependence on the incident energy, of the amount of each type of the point defects formed, and quantify unambiguously the threshold displacement energies for both O and Si atoms. These results provide a comprehensive basis to assess the nature and extent of radiation damage in quartz

Creep of Bulk C--S--H: Insights from Molecular Dynamics Simulations

Understanding the physical origin of creep in calcium--silicate--hydrate (C--S--H) is of primary importance, both for fundamental and practical interest. Here, we present a new method, based on molecular dynamics simulation, allowing us to simulate the long-term visco-elastic deformations of C--S--H. Under a given shear stress, C--S--H features a gradually increasing shear strain, which follows a logarithmic law. The computed creep modulus is found to be independent of the shear stress applied and is in excellent agreement with nanoindentation measurements, as extrapolated to zero porosity

Stretched Exponential Relaxation of Glasses at Low Temperature

The question of whether glass continues to relax at low temperature is of fundamental and practical interest. Here, we report a novel atomistic simulation method allowing us to directly access the long-term dynamics of glass relaxation at room temperature. We find that the potential energy relaxation follows a stretched exponential decay, with a stretching exponent $\beta = 3/5$, as predicted by Phillips' diffusion-trap model. Interestingly, volume relaxation is also found. However, it is not correlated to the energy relaxation, but is rather a manifestation of the mixed alkali effect

Predicting the dissolution kinetics of silicate glasses using machine learning

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, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties

Direct observation of pitting corrosion evolutions on carbon steel surfaces at the nano-to-micro- scales.

The Cl--induced corrosion of metals and alloys is of relevance to a wide range of engineered materials, structures, and systems. Because of the challenges in studying pitting corrosion in a quantitative and statistically significant manner, its kinetics remain poorly understood. Herein, by direct, nano- to micro-scale observations using vertical scanning interferometry (VSI), we examine the temporal evolution of pitting corrosion on AISI 1045 carbon steel over large surface areas in Cl--free, and Cl--enriched solutions. Special focus is paid to examine the nucleation and growth of pits, and the associated formation of roughened regions on steel surfaces. By statistical analysis of hundreds of individual pits, three stages of pitting corrosion, namely, induction, propagation, and saturation, are quantitatively distinguished. By quantifying the kinetics of these processes, we contextualize our current understanding of electrochemical corrosion within a framework that considers spatial dynamics and morphology evolutions. In the presence of Cl- ions, corrosion is highly accelerated due to multiple autocatalytic factors including destabilization of protective surface oxide films and preservation of aggressive microenvironments within the pits, both of which promote continued pit nucleation and growth. These findings offer new insights into predicting and modeling steel corrosion processes in mid-pH aqueous environments