Zaki, Mohd and Venugopal, Vineeth and Bhattoo, Ravinder and Bishnoi, Suresh and Singh, Sourabh Kumar and Allu, Amarnath R and Jayadeva, . and Krishnan, N M Anoop (2022) Interpreting the optical properties of oxide glasses with machine learning and Shapely additive explanations. Journal of the American Ceramic Society, 105 (6). pp. 4046-4057. ISSN 0002-7820

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Abstract

Due to their excellent optical properties, glasses are used for various applications ranging from smartphone screens to telescopes. Developing compositions with tailored Abbe number (V-d) and refractive index at 587.6 nm (n(d)), two crucial optical properties, is a major challenge. To this extent, machine learning (ML) approaches have been successfully used to develop composition-property models. However, these models are essentially black boxes in nature and suffer from the lack of interpretability. In this paper, we demonstrate the use of ML models to predict the composition-dependent variations of V-d and n(d). Further, using Shapely additive explanations (SHAP), we interpret the ML models to identify the contribution of each of the input components toward target prediction. We observe that glass formers such as SiO2, B2O3, and P2O5 and intermediates such as TiO2, PbO, and Bi2O3 play a significant role in controlling the optical properties. Interestingly, components contributing toward increasing the n(d) are found to decrease the V-d and vice versa. Finally, we develop the Abbe diagram, using the ML models, allowing accelerated discovery of new glasses for optical properties beyond the experimental pareto front. Overall, employing explainable ML, we predict and interpret the compositional control on the optical properties of oxide glasses.

Item Type:	Article
Subjects:	Glass
Divisions:	Glass
Depositing User:	Bidhan Chaudhuri
Date Deposited:	21 Sep 2023 09:06
Last Modified:	21 Sep 2023 09:06
URI:	http://cgcri.csircentral.net/id/eprint/5538

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