| About this Abstract |
| Meeting |
MS&T22: Materials Science & Technology
|
| Symposium
|
Ceramics and Glasses Modeling by Simulations and Machine Learning
|
| Presentation Title |
A Physics Informed Machine Learning Approach to Predict Glass Forming Ability |
| Author(s) |
Collin Wilkinson, Cory Trivelpiece, Rebecca Welch, John Mauro |
| On-Site Speaker (Planned) |
Collin Wilkinson |
| Abstract Scope |
Predicting the liquid compositions that will vitrify at experimentally accessible quench rates remains one of the grand challenges in the field of condensed matter physics. This glass-forming ability can be quantified as the critical quench rate needed to suppress crystallization. Knowledge of this critical quench rate also informs which glass composition could be used for new applications. There have been several physical and empirical models presented in the literature to predict the critical quench rate/glass-forming ability. These models range from those theoretically derived to those quantified only through experimental characterization. In this work, we instead propose a new method to calculate the critical quench rate using the recently developed physical models combined with machine learning. The results are then compared to traditional glass-forming ability metrics. |