Angular rigidity in tetrahedral network glasses

A set of oxide and chalcogenide tetrahedral glasses are investigated using molecular dynamics simulations. It is shown that unlike stoichiometric selenides such as GeSe$_2$ and SiSe$_2$, germania and silica display large standard deviations in the associated bond angle distributions. Within bond-bending constraints theory, this pattern can be interpreted as a manifestation of {\it {broken}} (i.e. ineffective) oxygen bond-bending constraints. The same analysis reveals that the changes in the Ge composition affects mostly bending around germanium in binary Ge-Se systems, leaving Se-centred bending almost unchanged. In contrast, the corresponding Se twisting (quantified by the dihedral angle) depends on the Ge composition and is reduced when the system becomes rigid. Our results establishes the atomic-scale foundations of the phenomelogical rigidity theory, thereby profoundly extending its significance and impact on the structural description of network glasses.Comment: 5 pages, 4 figure

Viscosity and viscosity anomalies of model silicates and magmas: a numerical investigation

We present results for transport properties (diffusion and viscosity) using computer simulations. Focus is made on a densified binary sodium disilicate 2SiO$_2$-Na$_2$O (NS2) liquid and on multicomponent magmatic liquids (MORB, basalt). In the NS2 liquid, results show that a certain number of anomalies appear when the system is densified: the usual diffusivity maxima/minima is found for the network-forming ions (Si,O) whereas the sodium atom displays three distinct r\'egimes for diffusion. Some of these features can be correlated with the obtained viscosity anomaly under pressure, the latter being be fairly well reproduced from the simulated diffusion constant. In model magmas (MORB liquid), we find a plateau followed by a continuous increase of the viscosity with pressure. Finally, having computed both diffusion and viscosity independently, we can discuss the validity of the Eyring equation for viscosity which relates diffusion and viscosity. It is shown that it can be considered as valid in melts with a high viscosity. On the overall, these results highlight the difficulty of establishing a firm relationship between dynamics, structure and thermodynamics in complex liquids.Comment: 13 pages, 8 figure

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

Structural, vibrational and thermal properties of densified silicates : insights from Molecular Dynamics

Structural, vibrational and thermal properties of densified sodium silicate (NS2) are investigated with classical molecular dynamics simulations of the glass and the liquid state. A systematic investigation of the glass structure with respect to density was performed. We observe a repolymerization of the network manifested by a transition from a tetrahedral to an octahedral silicon environment, the decrease of the amount of non-bridging oxygen atoms and the appearance of three-fold coordinated oxygen atoms (triclusters). Anomalous changes in the medium range order are observed, the first sharp diffraction peak showing a minimum of its full-width at half maximum according to density. The previously reported vibrational trends in densified glasses are observed, such as the shift of the Boson peak intensity to higher frequencies and the decrease of its intensity. Finally, we show that the thermal behavior of the liquid can be reproduced by the Birch-Murnaghan equation of states, thus allowing us to compute the isothermal compressibility

Predicting Young's Modulus of Glasses with Sparse Datasets using Machine Learning

Machine learning (ML) methods are becoming popular tools for the prediction and design of novel materials. In particular, neural network (NN) is a promising ML method, which can be used to identify hidden trends in the data. However, these methods rely on large datasets and often exhibit overfitting when used with sparse dataset. Further, assessing the uncertainty in predictions for a new dataset or an extrapolation of the present dataset is challenging. Herein, using Gaussian process regression (GPR), we predict Young's modulus for silicate glasses having sparse dataset. We show that GPR significantly outperforms NN for sparse dataset, while ensuring no overfitting. Further, thanks to the nonparametric nature, GPR provides quantitative bounds for the reliability of predictions while extrapolating. Overall, GPR presents an advanced ML methodology for accelerating the development of novel functional materials such as glasses.Comment: 17 pages, 5 figure

Order and disorder in calcium–silicate–hydrate

Despite advances in the characterization and modeling of cement hydrates, the atomic order in Calcium–Silicate–Hydrate (C–S–H), the binding phase of cement, remains an open question. Indeed, in contrast to the former crystalline model, recent molecular models suggest that the nanoscale structure of C–S–H is amorphous. To elucidate this issue, we analyzed the structure of a realistic simulated model of C–S–H, and compared the latter to crystalline tobermorite, a natural analogue of C–S–H, and to an artificial ideal glass. The results clearly indicate that C–S–H appears as amorphous, when averaged on all atoms. However, an analysis of the order around each atomic species reveals that its structure shows an intermediate degree of order, retaining some characteristics of the crystal while acquiring an overall glass-like disorder. Thanks to a detailed quantification of order and disorder, we show that, while C–S–H retains some signatures of a tobermorite-like layered structure, hydrated species are completely amorphous.ICoME2 Labex (ANR-11-LABX-0053)A*MIDEX projects (ANR-11-IDEX-0001-02)Program “Investissements d’Avenir