Molecular Modeling of Glassy Surfaces

Ori, Guido; Massobrio, Carlo; Bouzid, Assil; Coasne, Benoît

doi:10.1007/978-3-319-15675-0_13

Cited by 3 publications

(2 citation statements)

References 91 publications

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“…Atomic structural representations for the six CAS glasses in Table 1 have been generated following a commonly used “melt‐and‐quench” method in MD simulations, 56,57 as has been given in detail in several previous studies 37,38,58 . Briefly, we started by melting the structure in a simulation box containing about 4000 atoms (with similar chemical compositions as the experimental data, Table 1) at a temperature of 5000 K, and then progressively quenching the structure to 2000 K (over 1.5 ns) and then to 300 K (over 2 ns).…”

Section: Methodsmentioning

confidence: 99%

Data‐driven prediction of room‐temperature density for multicomponent silicate‐based glasses

Gong

Olivetti

2023

J Am Ceram Soc.

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Density is one of the most commonly measured or estimated material properties, especially for glasses and melts that are of significant interest to many fields, including metallurgy, geology, materials science, and sustainable cements. Here, three types of machine learning models (i.e., random forest [RF], artificial neural network [ANN], and Gaussian process regression [GPR]) have been developed to predict the room‐temperature density of glasses in the compositional space of CaO–MgO–Al2O3–SiO2–TiO2–FeO–Fe2O3–Na2O–K2O–MnO (CMASTFNKM), based on ∼2100 data points mined from ∼140 literature studies. The results show that the RF, ANN, and GPR models give accurate predictions of glass density with R2 values, root mean square error (RMSE), and mean absolute percentage error (MAPE) of ∼0.95–0.97, ∼0.03–0.04 g/cm3 and ∼0.63%–0.83%, respectively, for the 15% testing set, which are more accurate compared with empirical density models based on ionic packing ratio (with R2 values, RMSE, and MAPE of ∼0.28–0.91, ∼0.05–0.15 g/cm3, and ∼1.40%–4.61%, respectively). Furthermore, glass density is shown to be a reliable reactivity indicator for a range of CaO–Al2O3–SiO2 (CAS) and volcanic glasses due to its strong correlation (R2 values above ∼0.90) with the average metal–oxygen dissociation energy (a structural descriptor) of these glasses. Analysis of the predicted density–composition relationships from these models (for selected compositional subspaces) suggests that the single‐layer ANN and GPR models exhibit a certain level of transferability (i.e., ability to extrapolate to compositional spaces not [or less] covered in the database) and capture some known features, including the mixed alkaline earth (or alkali) effects.

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Section: Methodsmentioning

confidence: 99%

Data‐driven prediction of room‐temperature density for multicomponent silicate‐based glasses

Gong

Olivetti

2023

J Am Ceram Soc.

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“…21 It is, therefore, important to understand in more detail the features responsible for such differences and to explore how the dynamics of Na ions differ from those of other alkali ions, including Li. Molecular dynamics (MD) simulations appear helpful in this regard 24 and a recent ab initio and a classical study 22 on Na 2 S−SiS 2 glasses have made it possible to characterize the structure at selected compositions and temperatures.…”

Section: Introductionmentioning

confidence: 99%

Evidence for Complex Dynamics in Glassy Fast Ion Conductors: The Case of Sodium Thiosilicates

Sørensen,

Smedskjaer,

Micoulaut

2023

J. Phys. Chem. B

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Classical molecular dynamics is used to study the dynamics of alkali ions in a promising fast ion conductor glass system, i.e., Na2S–SiS2. Diffusion in such thiosilicates is found to display various salient features of alkali silicates, i.e., channel-like diffusion with typical length scales emerging as the temperature is decreased to the glassy state, and Arrhenius behavior for both Na ion diffusivity and calculated conductivity. The dynamics appears, however, to be largely heterogeneous as manifested by fast and slow Na ion motion at intermediate times, both in the high-temperature liquid and in the glassy state. In the former, a diffusion-limited regime is found due to the increased motion of the network-forming species that limits the Na ion dynamics, whereas at low temperatures, the typical dynamical heterogeneities are recovered as observed close to the glass transition.

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The hydrophilic-to-hydrophobic transition in glassy silica is driven by the atomic topology of its surface

Krishnan

Smedskjær

et al. 2018

The Journal of Chemical Physics

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The surface reactivity and hydrophilicity of silicate materials are key properties for various industrial applications. However, the structural origin of their affinity for water remains unclear. Here, based on reactive molecular dynamics simulations of a series of artificial glassy silica surfaces annealed at various temperatures and subsequently exposed to water, we show that silica exhibits a hydrophilic-to-hydrophobic transition driven by its silanol surface density. By applying topological constraint theory, we show that the surface reactivity and hydrophilic/hydrophobic character of silica are controlled by the atomic topology of its surface. This suggests that novel silicate materials with tailored reactivity and hydrophilicity could be developed through the topological nanoengineering of their surface.

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Molecular Modeling of Glassy Surfaces

Cited by 3 publications

References 91 publications

Data‐driven prediction of room‐temperature density for multicomponent silicate‐based glasses

Data‐driven prediction of room‐temperature density for multicomponent silicate‐based glasses

Evidence for Complex Dynamics in Glassy Fast Ion Conductors: The Case of Sodium Thiosilicates

The hydrophilic-to-hydrophobic transition in glassy silica is driven by the atomic topology of its surface

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