Although molecular dynamics (MD) simulations are commonly used to predict the structure and properties of glasses, they are intrinsically limited to short time scales, necessitating the use of fast cooling rates. It is therefore challenging to compare results from MD simulations to experimental results for glasses cooled on typical laboratory time scales. Based on MD simulations of a sodium silicate glass with varying cooling rate (from 0.01 to 100 K/ps), here we show that thermal history primarily affects the medium-range order structure, while the shortrange order is largely unaffected over the range of cooling rates simulated. This results in a decoupling between the enthalpy and volume relaxation functions, where the enthalpy quickly plateaus as the cooling rate decreases, whereas density exhibits a slower relaxation. Finally, we demonstrate that the outcomes of MD simulations can be meaningfully compared to experimental values if properly extrapolated to slower cooling rates.

The application of machine learning to predict materials’ properties usually requires a large number of consistent data for training. However, experimental datasets of high quality are not always available or self-consistent. Here, as an alternative route, we combine machine learning with high-throughput molecular dynamics simulations to predict the Young’s modulus of silicate glasses. We demonstrate that this combined approach offers good and reliable predictions over the entire compositional domain. By comparing the performances of select machine learning algorithms, we discuss the nature of the balance between accuracy, simplicity, and interpretability in machine learning.

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Fly ash, a byproduct of coal combustion, can be used as supplementary cementitious material (SCM) to replace ordinary portland cement (OPC) in concrete. This generates revenue for coal power plant operators and also reduces the CO 2 intensity of the binder fraction of a concrete (each ton of OPC replaced by fly ash results in 0.9 ton of avoided CO 2 emissions, if the fly ash is considered to have no carbon footprint). However, the use of fly ash in concrete has thus far been limited to replacement levels less than 20 mass % due to uncertainties in their performance as SCM. Although the ability of a fly ash to replace cement in concrete is largely determined by the reactivity of its amorphous phase, characterizing fly ashes' amorphous phase is complex and cost prohibitive, which has thus far prevented any high-throughput screening of fly ashes to assess their suitability as SCMs. Here, we introduce a machine-learning-based methodology that enables robust screening of reactive fly ashes based solely on fast, inexpensive bulk characterization (X-ray fluorescence: XRF), by using the network topology of fly ashes' amorphous phase as a structural proxy for their reactivity. On the basis of a data set of more than 100 fly ashes, we train an artificial neural network (ANN) model that offers accurate predictions of the mass fraction of fly ashes' amorphous phase and the network topology thereof. This new method seeks to maximize the beneficial use of fly ashes obtained from routine production, as well as to identify opportunities for the reclamation of ashes that are presently stored in impoundments.

Fly ash, an aluminosilicate composite consisting of disordered (major) and crystalline (minor) compounds, is a low‐carbon alternative that can partially replace ordinary portland cement (OPC) in the binder fraction of concrete. Therefore, understanding the reactivity of fly ash in the hyperalkaline conditions prevalent in concrete is critical to predicting concrete's performance; including setting and strength gain. Herein, temporal measurements of the solution composition (using inductively coupled plasma‐optical emission spectrometry: ICP‐OES) are used to assess the aqueous dissolution rate of monophasic synthetic aluminosilicate glasses analogous to those present in technical fly ashes, under hyperalkaline conditions (10 ≤ pH ≤ 13) across a range of temperatures (25°C ≤ T≤45°C). The dissolution rate is shown to depend on the average number of topological constraints per atom within the glass network (nc, unitless), but this dependence weakens with increasing pH (>10). This is postulated to be on account of: (a) time‐dependent changes in the glass’ surface structure, that is, the number of topological constraints; and/or (b) a change in the dissolution mechanism (eg from network hydrolysis to transport control). The results indicate that the topological description of glass dissolution is most rigorously valid only at very short reaction times (ie at high undersaturations), especially under conditions of hyperalkalinity. These findings provide an improved basis to understand the underlying factors that affect the initial and ongoing reactivity of aluminosilicate glasses such as fly ash in changing chemical environments, for example, when such materials are utilized in cementitious composites.

The extent to which carbon dioxide (CO 2 ) mineralization ("carbonation") using alkaline solids can reduce atmospheric CO 2 concentrations is dictated by the rate of divalent alkaline metal release from such solids. These solids have distinct reactivities, that is, bulk dissolution rates, which dictate their rates of carbonation. To assess the feasibility of utilizing alkaline solids to mitigate CO 2 emissions at scale, assessments of practical carbonation potentials under ambient conditions, which are often distinct from their stoichiometric carbonation potential as described by their bulk chemical composition, are needed. Therefore, the carbonation (or "CO 2 mineralization") potentials of 16 naturally occurring (mafic and ultramafic) rocks and industrial alkaline solids (fly ashes and slags) were quantified. In general, the extent of carbonation for the pulverized and as-received solids which is achievable under ambient conditions [25 °C, 1 bar]in the presence of excess CO 2 and waterthat is, the carbonation potential, is correlated with the CaO and MgO content and varies inversely with the SiO 2 content. Particularly, the carbonation efficiency (i.e., the ratio of the measured to the stoichiometric carbonation potential) is controlled by the atomic topology (network connectivity) of the solid reactant suggesting that network rupture is the rate-controlling step of dissolution and, hence, carbonation. Based on our data, we offer estimates of CO 2 removal that can be achieved under ambient exposure conditions to assess the controls and capacity of ambient CO 2 mineralization as a carbon dioxide removal strategy.