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Meeting MS&T21: Materials Science & Technology
Symposium Ceramics and Glasses Modeling by Simulations and Machine Learning
Presentation Title Elucidating Compositional Governance of Optical Properties of Oxide Glasses Using Interpretable Machine Learning
Author(s) Mohd Zaki, Vineeth Venugopal, Ravinder Bhattoo, Suresh Bishnoi, Sourabh Kumar Singh, Amarnath R. Allu, Jayadeva , N. M. Anoop Krishnan
On-Site Speaker (Planned) Mohd Zaki
Abstract Scope 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.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Machine-learning Based Hierarchical Framework to Discover Novel Scintillator Chemistries
Bayesian Optimization of Silicon Nitride Empirical Potentials
Ceramics from Polymers –– Results of Ab Initio Molecular Dynamic Simulations
Deciphering the Viscosity of Glass Materials with Machine Learning
Decomposing the Strength of Hydrated Cement Compositions by Machine Learning
Development of a Reactive Force Field (ReaxFF) for Simulation of Polymer-derived Ceramics
Development of a Transferable Inter-atomic Potential for Boroaluminosilicate Glasses
Effect of Polydispersity on the Fracture Properties of Calcium–Silicate–Hydrate Gel
Elucidating Compositional Governance of Optical Properties of Oxide Glasses Using Interpretable Machine Learning
Fusing Experimental and Simulation Datasets in Machine Learning for Predicting Glass Properties
Graph ODE for Learning Dynamic Systems
Impact of Irradiation on the Properties of Gel Layer Formed After Aqueous Corrosion of Borosilicate Glasses
Kinetic Monte Carlo Simulation of Glasses Aided by Machine Learning
Looking for Order in Disorder: Topological Data Analysis of Glass Structure
Machine Learning as a Tool to Accelerate the Design of Nuclear Waste Glasses with Enhanced Sulfur Loadings
Modeling Polaron Hopping in Ternary Spinel Oxides
Now On-Demand Only: Information Extraction Pipeline for Glasses: An NLP Based Approach
P1-3: Molecular Dynamic Characteristic Temperatures for Predicting Metallic Glass Forming Ability
The Energy Landscape Governs Ductility in Disordered Materials
Toward Revealing Full Atomic Picture of Nanoindentation Deformation Mechanisms in Li2O-2SiO2 Glass-ceramics

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