About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Glass Forming Ability of Silica Glasses with Machine Learning Based Prediction Technique |
Author(s) |
Jong Ho Kim |
On-Site Speaker (Planned) |
Jong Ho Kim |
Abstract Scope |
Silica glass is the most common and widely used material. In particular, when a specific element is added to silica glass, it forms a glass ceramic and has low thermal expansion characteristics, so it is used in various applications. For the properties of silica glass, amorphization is important and can be predicted through the glass forming ability. It is difficult to obtain sufficient data to measure the glass forming ability because it is experimentally based on various compositions and cooling conditions. Therefore, many researchers have conducted research to predict the forming ability through several physical properties. In this study, we tried to predict the glass forming ability using machine learning techniques and an open database. By applying several machine learning techniques, the accuracy was compared and the optimal model was selected. Based on this result, a composition that can be manufactured more easily and can maintain amorphous properties is suggested. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Ceramics, Computational Materials Science & Engineering |