The role of the network-modifier's field-strength in the chemical durability of aluminoborate glasses

Oey, Tandré; Frederiksen, Kristine F.; Mascaraque, Nerea; Youngman, Randall E.; Balonis, Magdalena; Smedskjær, Morten Mattrup; Bauchy, Mathieu; Sant, Gaurav

doi:10.1016/j.jnoncrysol.2018.11.019

Cited by 33 publications

(25 citation statements)

References 36 publications

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“…21,[38][39][40][41][42][43] Importantly, the effective activation energy of dissolution for a fixed pH has recently been suggested to be proportional to n c . 31,33,[44][45][46][47][48][49][50] Based on these findings, we compute the number of topological constraints of the rigid aluminosilicate network n c for each glass (see Methods section) and use it as a descriptor of the atomic structure. As shown in Fig.…”

Section: Topology-informed Reduced-dimensionality Descriptorsmentioning

confidence: 99%

“…5, we observe that, at fixed pH, the dissolution rate is indeed largely correlated to n c , which supports the use of this metric as an input to the model. We then define Model IV, which expresses the logarithm of the dissolution rate in terms of pH, n c , and the fraction of network modifiers (i.e., Na 2 O)as the network modifiers are not explicitly accounted for in the number of topological constraints of the rigid aluminosilicate network (see Methods): 47 Model IV : logðDRÞ ¼ f ðpH acid ; pH base ; n c ; Na 2 OÞ (4) Figure 6a shows the RRMSE of the training and validation sets as a function of the maximum polynomial degree p for Model IV. Like Model III, we note that a linear model (i.e., p = 1) offers the best performance.…”

Section: Topology-informed Reduced-dimensionality Descriptorsmentioning

confidence: 99%

“…A previous study recently suggested that the dissolution rate is related to the number of constraints per atom in the "skeleton" network (that is, that formed by the network-forming species, i.e., Si and O here) rather than to the number of constraints per atom in the whole network (that is, including the network-modifying species, i.e., Na here). 47 Based on this, we enumerate the number of constraints per atom in (Na 2 O) x (Al 2 O 3 ) y (SiO 2 ) 1-x-y ) as follows. (i) Each Si creates four BS constraints with its four surrounding O neighbors and five BB constraints (i.e., the number of independent angles that needs to be fixed to define the tetrahedral angular environment of Si atoms).…”

Section: Topological Constraints Enumerationmentioning

confidence: 99%

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Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning

Liu¹,

Zhang²,

Krishnan³

et al. 2019

npj Mater Degrad

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Machine learning (ML) regression methods are promising tools to develop models predicting the properties of materials by learning from existing databases. However, although ML models are usually good at interpolating data, they often do not offer reliable extrapolations and can violate the laws of physics. Here, to address the limitations of traditional ML, we introduce a "topologyinformed ML" paradigm-wherein some features of the network topology (rather than traditional descriptors) are used as fingerprint for ML models-and apply this method to predict the forward (stage I) dissolution rate of a series of silicate glasses. We demonstrate that relying on a topological description of the atomic network (i) increases the accuracy of the predictions, (ii) enhances the simplicity and interpretability of the predictive models, (iii) reduces the need for large training sets, and (iv) improves the ability of the models to extrapolate predictions far from their training sets. As such, topology-informed ML can overcome the limitations facing traditional ML (e.g., accuracy vs. simplicity tradeoff) and offers a promising route to predict the properties of materials in a robust fashion.

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Section: Topology-informed Reduced-dimensionality Descriptorsmentioning

confidence: 99%

Section: Topology-informed Reduced-dimensionality Descriptorsmentioning

confidence: 99%

Section: Topological Constraints Enumerationmentioning

confidence: 99%

See 1 more Smart Citation

Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning

Liu¹,

Zhang²,

Krishnan³

et al. 2019

npj Mater Degrad

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“…49,50 Coming back to chemical durability (dissolution rates), recently Pignatelli et al and Hsiao et al have shown that mineral and glass dissolution rates are, to the first order, controlled by the topology of their atomic networks. 9,51 Specifically, it has been highlighted that dissolution rates, for a given solution composition, are determined by the number of topological constraints per atom (n c , unitless) as represented by an Arrhenius-like function: [51][52][53][54] constant that depends on the solution chemistry, R is the gas constant, E 0 is the energy required to break a unit topological constraint and T is the thermodynamic temperature. As such, the variation in the dissolution rates before and following irradiation can be expressed as:…”

Section: Dissolution Behavior Of Pristine and Irradiated Carbonates Imentioning

confidence: 99%

The effect of irradiation on the atomic structure and chemical durability of calcite and dolomite

et al. 2019

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When exposed to irradiation-e.g., in nuclear power plant environments-minerals may experience alterations in their atomic structure which, in turn, result in changes in their physical and chemical properties. Herein, we mimic via Ar + implantation the effects of neutron irradiation on calcite (CaCO 3 ) and dolomite (CaMg(CO 3 ) 2 )two carbonate minerals that often find use as aggregates in concrete: a material that is extensively used in the construction of critical structural and safety components in nuclear power plants. By a pioneering combination of nanoscale quantifications of mineral dissolution rates (i.e., a proxy for chemical durability) in alkaline solutions, vibrational (infrared and Raman) spectroscopy, and molecular simulations, we find that irradiation minimally affects the atomic structure and properties of these carbonate minerals. This insensitivity to radiation arises from the predominantly ionic nature of the interatomic bonds in these minerals which can relax and recover their initial configuration, thus ensuring minimal damage and permanent alterations to these minerals following radiation exposure. The outcomes have significant implications on the selection, use, and specification of mineral aggregates for use in nuclear concrete construction.

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“…To address such difficulties, topological constraint theory (TCT) provides a simplified framework to predict the properties of glasses based on the topology of their atomic network [14][15][16] . Recently, this approach has been used to predict the dissolution rate of silicate minerals and glasses under varying pH conditions [17][18][19][20][21][22] .…”

Section: Introductionmentioning

confidence: 99%

Can a simple topological-constraints-based model predict the initial dissolution rate of borosilicate and aluminosilicate glasses?

et al. 2020

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Tuning glass composition to obtain targeted properties generally relies on empirical approaches. However, a deep understanding of the physical and chemical mechanisms linking glass composition to its structure and properties would enable developing reliable predictive models. Indeed, although empirical models are usually able to interpolate composition-property relationships within a given compositional envelope, they often fail at extrapolating predictions far from their training domain. Here, as an alternative route to empirical models, we show that a structural descriptor based on the number of topological constraints per atom can be used to predict the initial dissolution rate of aluminosilicate and borosilicate glasses after being parameterized on different families of glasses (specific series of borosilicate glasses). Sixteen glasses belonging to these families were studied and their initial dissolution rates were determined at 90°C and pH 90°C = 9, covering rates spanning over 5 orders of magnitude. The model based on topological constraints was trained based on seven select borosilicate glasses (R 2 = 0.997) and used to predict the dissolution rate of nine additional borosilicate and aluminosilicate glasses. We show that, provided that corrections are made for high alkali content glasses that dissolve incongruently (preferential release of Na), the model gives reasonable predictions, even far from its training domain.npj Materials Degradation (2020) 4:6 ; https://doi.

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The role of the network-modifier's field-strength in the chemical durability of aluminoborate glasses

Cited by 33 publications

References 36 publications

Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning

Predicting the dissolution kinetics of silicate glasses by topology-informed machine learning

The effect of irradiation on the atomic structure and chemical durability of calcite and dolomite

Can a simple topological-constraints-based model predict the initial dissolution rate of borosilicate and aluminosilicate glasses?

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