| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Artificial Intelligence for Materials Design and Process Optimization
|
| Presentation Title |
Physics-Informed Machine Learning for Predicting Glass Properties |
| Author(s) |
Kai Yang, Han Liu, Mathieu Bauchy |
| On-Site Speaker (Planned) |
Mathieu Bauchy |
| Abstract Scope |
Data-driven modeling based on machine learning (ML) offers a promising route to develop robust composition-property models in glasses. However, traditional ML shows several limitations: (i) it requires a large amount of consistent data, (ii) it has a poor ability for extrapolation far from the training set, and (iii) it can potentially violate physics laws. To address these limitations, we present a new physics-informed ML framework that simultaneously leverages experimental measurements, simulation data, and physical knowledge. We show informing ML with physics-based knowledge greatly enhances the ability of ML models to extrapolate predictions from their training set—which is key to discover new glasses featuring properties that are very different from present glasses. |