Deep learning aided rational design of oxide glasses

['R Ravinder', 'KH Sridhara', 'S Bishnoi', 'HS Grover']

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Abstract

Designing new glasses requires a priori knowledge of how the composition of a glass dictates its properties such as stiffness, density, or processability. Thus, accelerated design of glasses for targeted applications remain impeded due to the lack of composition–property models. To this extent, exploiting a large dataset of glasses comprising of up to 37 oxide components and more than 100 000 glass compositions, we develop high-fidelity deep neural networks for the prediction of eight properties that enable the design of glasses

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