Fracture toughness anomalies: Viewpoint of topological constraint theory

Bauchy, Mathieu; Wang, Bu; Wang, Mengyi; Yu, Yingtian; Qomi, Mohammad Javad Abdolhosseini; Smedskjær, Morten Mattrup; Bichara, Christophe; Ulm, Franz‐Josef; Pellenq, Roland J.‐M.

doi:10.1016/j.actamat.2016.09.004

Cited by 88 publications

(75 citation statements)

References 60 publications

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“…On the other hand, irradiated quartz exhibits a B of 0.21, confirming the dominance of plastic deformation during failure. Note that a similar approach has been used to quantify the nanoscale ductility in other silicate glasses and cementitious materials …”

Section: Resultsmentioning

confidence: 99%

Irradiation‐induced brittle‐to‐ductile transition in α‐quartz

Rena

Kumar

et al. 2019

J Am Ceram Soc

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Silicate materials such as quartz and silica glass are inherently brittle in nature. Here, using atomistic simulations, we demonstrate that the quartz crystal exhibits a brittle‐to‐ductile transition when irradiated. We show that the nanoscale plasticity observed in the irradiated structure is reminiscent of metal‐like plasticity, which is neither observed in the isochemical crystalline or glassy structure. Invoking an energy landscape approach, we demonstrate that the local atomic self‐organization facilitated by the shallow energy landscape is at the origin of this plastic behavior. Overall, the results suggest that the irradiation could be a methodology to induce a ductile transition in materials that are otherwise brittle.

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Section: Resultsmentioning

confidence: 99%

Irradiation‐induced brittle‐to‐ductile transition in α‐quartz

Rena

Kumar

et al. 2019

J Am Ceram Soc

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“…21,[35][36][37] Based on this framework, the number of topological constraints per atom (n c ) has been shown to offer a useful reduced-dimensionality descriptor that captures the connectivity of the atomic network and, hence, can be used to predict various glass properties, e.g., hardness, stiffness, fracture toughness, glass transition temperature, fragility, etc. 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.…”

Section: Topology-informed Reduced-dimensionality Descriptorsmentioning

confidence: 99%

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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“…Although no topological model predicting the compositional dependence of the fracture of glasses has been proposed thus far, various glasses have been noted to exhibit maximum fracture toughness at the isostatic threshold (see Fig. 13) [45,108]. This has been suggested to arise from the fact that (i) flexible glasses (nc < 3) exhibit low cohesion (low surface energy) due to their low connectivity, (ii) stressed-rigid glasses (nc > 3) break in a brittle fashion as their high connectivity prevents any ductile atomic reorganization, whereas (iii) isostatic glasses (nc = 3) exhibit the best balance between cohesion and ability to plastically deform [45].…”

Section: Fracture Toughnessmentioning

confidence: 99%

Topological Constraint Theory and Rigidity of Glasses

Bauchy¹

2019

21st Century Nanoscience – A Handbook

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Topological constraint theory has become an increasingly popular tool to predict the compositional dependence of glass properties or pinpoint promising compositions with tailored functionalities. This approach reduces complex disordered networks into simpler mechanical trusses. Thereby, topological constraint theory captures the important atomic topology that controls macroscopic properties while filtering out less relevant second-order structural details. As such, topological constraint theory can be used to decode the genome of glass, that is, to identify and decipher how the basic structural building blocks of glasses control their engineering properties-in the same way as the human genome offers information that serves as a blueprint for an individual's growth and development. Thanks to its elegance and simplicity, topological constraint theory has enabled the development of various physics-based models that can analytically predict various properties of glass. In this Chapter, I introduce some general background in glass science, concepts of atomic rigidity, and topological constraint theory. The topological constraints enumeration scheme is presented for various archetypical glasses and is used to understand the origin of their glass-forming ability. Finally, various topological models enabling the prediction of glass properties are reviewed, with a focus on hardness, fracture toughness, viscosity, fragility, glass transition temperature, and dissolution kinetics. Time Scientific progress Fig. 1: The discovery of new materials largely defines the progress of our civilization. Glass genome and discovery of new compositionsRevealing the full potential of glass requires the discovery of new glass formulations showing unique properties. However, this task is especially complicated for non-crystalline glassy materials for several reasons. First, virtually all the elements of the periodic table can be turned into a glass, if cooled fast enough from the liquid state [9]. Second, unlike crystals, glasses do not have to satisfy any fixed stoichiometry thanks to their out-of-equilibrium nature. As such, the composition of glasses can be continuously changed. For all these reasons, the number of possible glass compositions has been estimated to be around 1052! Yet, only about 200,000 glass compositions have been produced in the last 6000 years of human glass history [9]. These numbers demonstrate that the range of possible glasses remains largely unexplored-so that there exists an incredible opportunity for the future discovery of new glass formulations with unusual functionalities.However, although it offers a large room for improvement, the astronomical number of possible glass compositions is also a challenge. Indeed, such a large parametric space renders traditional Edisonian discovery approach based on trial-and-error largely inefficient. To accelerate the discovery of new glass formulations, it is necessary to decode the Glass Genome, that is, to decipher how the properties of glasses are controlled by their und...

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Fracture toughness anomalies: Viewpoint of topological constraint theory

Cited by 88 publications

References 60 publications

Irradiation‐induced brittle‐to‐ductile transition in α‐quartz

Irradiation‐induced brittle‐to‐ductile transition in α‐quartz

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

Topological Constraint Theory and Rigidity of Glasses

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