About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
|
Materials Processing and Fundamental Understanding Based on Machine Learning and Data Informatics
|
Presentation Title |
AI Driven Microscopic Analysis to Predict the Local Structure in Zirconia Ceramics |
Author(s) |
Prachi Garg, Kristofer Reyes, Baishakhi Mazumder |
On-Site Speaker (Planned) |
Prachi Garg |
Abstract Scope |
Tetragonal yttria stabilized zirconia (t-YSZ) is widely applied in dental restoration and solid oxide fuel cells due to their excellent properties. To increase the phase structure and phase stability of t-YSZ, a critical vacancy concentration is required. Annihilated vacancies cause the degradation of the oxide material driving the importance of vacancy mapping and detection. However, it is challenging to detect vacancies in oxide materials using conventional microscopy tool. Atom probe microscopy, a three-dimensional nano-analytical tool can provide atomic positions along with structural and chemical information, however is limited to vacancy detection. We applied deep learning model on the microscopy data, which was trained on synthetic data generated by crystal simulations using an empirical ball-and-spring model. The trained AI model will automatically analyze the real APT data to learn and predict the local structure including vacancies to predict the mechanical stability of the material. |