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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.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Addressing Data Needs for High Temperature Material Processing with Natural Language Processing
AI Driven Microscopic Analysis to Predict the Local Structure in Zirconia Ceramics
AI/ML-Driven Multi-Scale Modeling and Design of Structural Materials
B-5: Using Computer Vision and Machine Learning to Characterize Melt Pool Geometry in Additive Manufacturing
Comparison of Data Driven and Physics-informed Machine Learning Models for Temperature Prediction of Shear Assisted Processing and Extrusion
Composition and Property Prediction of Polymer-derived Silicon Oxycarbides
Computational and Machine Learning Studies of DNA-templated Dye Aggregate Design
Data-Driven Study of Shape Memory Behavior of Multi-component Ni-Ti Alloys
Graph Neural Network Modeling of Deforming Polycrystals
High-throughput Machine Learning Experiments with Graph Neural Networks for Predicting Abnormal Grain Growth in Polycrystalline Materials
Large Scale Atomistic Simulation of the B1-B4 Phase Transition of GaN with the Machine Learning Potential
Machine Learning Based Prediction of Cation Distribution in Complex Spinel Oxides as a Function of Processing Temperature
Machine Learning for Joint Quality Performance-determining Relationship between Intermetallic Properties and weld Microstructure of Al/steel Resistance Spot Welds
Microstructure Characterization and Reconstruction by Deep Learning Methodology
Unraveling the Process Fundamentals of Additive Friction Stir Deposition by Integrating Physics Simulation with Data-driven Approaches

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