| About this Abstract |
| 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. |