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Meeting MS&T21: Materials Science & Technology
Symposium AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
Presentation Title Understanding the Composition–property Relationship of Glasses Using Interpretable Machine Learning
Author(s) Ravinder Bhattoo, Suresh Bishnoi, Mohd Zaki, N. M. Anoop Krishnan
On-Site Speaker (Planned) Ravinder Bhattoo
Abstract Scope The property of inorganic glass is significantly affected by its stoichiometry. Therefore, understanding the composition–property relationship is key for developing novel inorganic glasses. Herein, we use a glass database (>450,000 glass compositions) with up to 232 glass components to train XGBoost (Extreme Gradient Boosting) models for 25 glass properties (including optical, physical, electrical, and mechanical properties). Further, we use SHAP (Shapely additive explanations) to determine each input glass component’s role in controlling the glass property quantitatively. The SHAP analysis reveals a strong interdependence among the glass components for properties like liquidus temperature and glass transition, whereas no such interdependence for properties like density. While some of this interdependence can be explained as “boron anomaly” and “mixed modifier effect”, the others need further exploration. Thus, our work is critical in understanding the component–structure–property relationship of inorganic glasses and discovering novel inorganic glasses.
Proceedings Inclusion? Undecided

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A Deep Generative Model for Parametric EBSD Pattern Simulation
Aluminum Alloy Design Using Physics Informed Machine Learning
De Novo Inverse Design of Nanoporous Materials by Machine Learning
Deep Learning and Uncertainty Quantification for Automated Experiments
Discovery of Novel Crystal Structures via Generative Adversarial Networks
Improving EBM NIR Image Analysis for Component Qualification a Statistical Learning Approach
Machine-learning Based Algorithms for 4D X-ray Microtomographic Analysis
Machine Learning for Automated Experiment in Scanning Probe and Electron Microscopy
Machine Learning Polymer Property Prediction Models with Polymers Represented as Natural Language
Non-iterative Deep Learning for High-fidelity Microscopic Tomography
Optimizing the Training of Convolutional Neural Networks for Image Segmentation
Prediction of Dynamic Properties of LiF and FLiBe Molten Salts with DeepPot Network Potentials
Refinements to the Production of Machine Learning Interatomic Potentials
Semantic Segmentation of Porosity in In-situ X-ray Tomography Data Using FCNs
Tuning Optoelectronic Properties of Semiconductors with First Principles Modeling and Machine Learning
Understanding the Composition–property Relationship of Glasses Using Interpretable Machine Learning

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