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Creators/Authors contains: "Krishnan, N. M. Anoop"

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  1. Abstract

    Nanoindentation is a widely used method to probe the mechanical properties of glasses. However, interpreting glasses’ response to nanoindentation can be challenging due to the complex nature of the stress field under the indenter tip and the lack ofin situcharacterization techniques. Here, we present a numerical model describing the nanoindentation of an archetypical soda‐lime silicate window glass by means of peridynamic simulations. We show that, although it does not capture shear flow and permanent densification, peridynamics exhibits a good agreement with experimental nanoindentation data and offers a direct access to the stress field forming under the indenter tip.

     
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  2. Abstract

    Glasses have been an integral part of human life for more than 2000 years. Despite several years of research and analysis, some fundamental and practical questions on glasses still remain unanswered. While most of the earlier approaches were based on (i) expert knowledge and intuition, (ii) Edisonian trial and error, or (iii) physics‐driven modeling and analysis, recent studies suggest that data‐driven techniques, such as artificial intelligence (AI) and machine learning (ML), can provide fresh perspectives to tackle some of these questions. In this article, we identify 21 grand challenges in glass science, the solutions of which are either enabling AI and ML or enabled by AI and ML to accelerate the field of glass science. The challenges presented here range from fundamental questions related to glass formation and composition–processing–property relationships to industrial problems such as automated flaw detection in glass manufacturing. We believe that the present article will instill enthusiasm among the readers to explore some of the grand challenges outlined here and to discover many more challenges that can advance the field of glass science, engineering, and technology.

     
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  3. Abstract

    Topological constraint theory (TCT) has enabled the prediction of various properties of oxide glasses as a function of their composition and structure. However, the robust application of TCT relies on accurate knowledge of the network structure and topology. Here, based on classical molecular dynamics simulations, we derive a fully analytical model describing the topology of the calcium aluminosilicate [(CaO)x(Al2O3)y(SiO2)1−xy, CAS] ternary system. This model yields the state of rigidity (flexible, isostatic, or stressed‐rigid) of CAS systems as a function of composition and temperature. These results reveal the existence of correlations between network topology and glass‐forming ability. This study suggests that glass‐forming ability is encoded in the network topology of the liquid state rather than that of the glassy state.

     
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