The medium-range order (MRO) in amorphous systems has been linked to complex features such as the dynamic heterogeneities in supercooled liquids or the plastic deformation of glasses. However, the nature of the MRO in these materials has remained elusive, primarily due to the lack of methods capable of characterizing this order. Here, we leverage standard two-body structural correlators and advanced many-body correlation functions to probe numerically the MRO in prototypical network glass formers, i.e., silica and sodium silicates, systems that are of great importance in natural as well as industrial settings. With increasing Na concentration, one finds that the local environment of Na becomes more structured and the spatial distribution of Na on intermediate length scales changes from blob-like to channel-like, indicating a growing inhomogeneity in the spatial Na arrangement. In parallel, we find that the Si-O network becomes increasingly depolymerized, resulting in a ring size distribution that broadens. The radius of gyration of the rings is well described by a power law with an exponent around 0.75, indicating that the rings are progressively more crumpled with increasing size. Using a recently proposed four-point correlation function, we reveal that the relative orientation of the tetrahedra shows a surprising transition at a distance around 4 Å, a structural modification that is not seen in standard two-point correlation functions. The order induced by this transition propagates to larger distances, thus affecting the structure on intermediate length scales. Furthermore, we find that, for Na-rich samples the length scale characterizing the MRO is nonmonotonic as a function of temperature, caused by the competition between energetic and entropic terms which makes that the sample forms complex mesoscpic domains. Finally, we demonstrate that the structural correlation lengths as obtained from the correlation functions that quantify the MRO are correlated with macroscopic observables such as the kinetic fragility of the liquids and the elastic properties of the glasses. These findings allow to reach a deeper understanding of the nature of the MRO in network glass formers, insight that is crucial for establishing quantitative relations between their MRO and macroscopic properties.

Despite the many experimental, theoretical, and numerical studies, there are still many unknowns about the microscopic understanding of the surface properties of silicate glasses. Nowadays, many applications rely on the control of surface composition, local structure, or morphology. Moreover, the disordered nature of the glass structure makes exploring and rationalizing some of these properties difficult on a microscopic level. MD simulations have been carried out to systematically examine the influence of alkali content and their chemical nature on the properties of alkali glass surfaces. We have shown how the production history, whether it's a melt-quench process or dynamic fracture, has a direct impact on both the atomistic-level and morphological properties

Nowdays there is an increasing need to control the properties of glasses and the ones of their surfaces, in particular. A considerable number of experimental and computational studies have been conducted in order to better describe the surface features of silicate glasses. Nevertheless there is still a lack of a deeper insight on how the surface topography and the structural environments depend on glass composition and/or production history. etc.Using molecular dynamics simulations, we have investigated the surface properties of alkali silicate glasses. We have studied two types of surfaces, a melt-formed surface and a fracture surface and the influence of the alkali modifier has been rationalized in terms of the interplay between multiple factors involving the size of the ions, bond strength, and charge balance on the surface. Specifically, we have also studied the effects of the glass composition and alkali modifier on the topographical properties of the two types of surfaces

We use molecular dynamics simulations to study the structural and mechanical properties of bead-spring polymer networks. In this study we deal with systems which are biologically-relevant as they result from the action of enzymes, i.e. biological catalysts. The latter convert repulsive monomers into attractive ones and hence, starting from a polymer solution, trigger the formation of a physically-crosslinked polymer network. This gel has a remarkably regular mesostructure in the form of a cluster phase. We simulate uniaxial tension of these networks. The evolution of their structural and mechanical properties during deformation is monitored by computing quantities such as the anisotropic pair correlation functions, Poisson's ratio, elastic moduli and stress-strain curves, and the effects of temperature and system composition (i.e. fraction of attractive monomers) are investigated.

Abstract Creating amorphous solid states by randomly bonding an ensemble of dense liquid monomers is a common procedure that is used to create a variety of materials, such as epoxy resins, colloidal gels, and vitrimers. However, the properties of the resulting solid do a priori strongly depend on the preparation history. This can lead to substantial aging of the material; for example, properties such as mechanical moduli and transport coefficients rely on the time elapsed since solidification, which can lead to a slow degradation of the material in technological applications. It is therefore important to understand under which conditions random monomer bonding can lead to stable solid states, that is, long-lived metastable states whose properties do not change over time. This work presents a theoretical and computational analysis of this problem and introduces a random bonding procedure that ensures the proper equilibration of the resulting amorphous states. Our procedure also provides a new route to investigate the fundamental properties of glassy energy landscapes by producing translationally invariant ultrastable glassy states in simple particle models.