|About this Abstract
||MS&T21: Materials Science & Technology
||Ceramics and Glasses Modeling by Simulations and Machine Learning
||Elucidating Compositional Governance of Optical Properties of Oxide Glasses Using Interpretable Machine Learning
||Mohd Zaki, Vineeth Venugopal, Ravinder Bhattoo, Suresh Bishnoi, Sourabh Kumar Singh, Amarnath R. Allu, Jayadeva , N. M. Anoop Krishnan
|On-Site Speaker (Planned)
Glasses are wide used ranging from display devices to scientific instruments like microscope and telescopes owing to their excellent optical properties. The optical properties of Abbe number (Vd) and refractive index at 587.6 nm (nd) control how the light behaves when it passes through the glass. To eliminate guesswork in experimentally preparing glasses and accelerate the discovery of glass compositions with desirable Vd and nd, we present machine learning (ML) approach to predict optical properties of inorganic glasses with chemical oxides as input. Due to black-box nature of ML models, it is highly impossible to decipher the composition-properties relationship. Here, we have used Shapely Additive exPlanations (SHAP) to elucidate the ML model predictions and decode the influence of chemical oxides on the predicted values of Vd and nd. Hence, this work presents a roadmap for researchers to decode the compositional control of different properties of inorganic glasses.