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Meeting MS&T22: Materials Science & Technology
Symposium Materials Informatics for Images and Multi-dimensional Datasets
Presentation Title FeCrAl Alloy Design Utilizing Literature, Experiments, High Throughput Characterization, and Machine Learning
Author(s) Sandipp Krishnan Ravi, Andrew Hoffman, Rajnikant Umretiya, Bojun Feng, Subhrajit Roychowdhury, Sayan Ghosh, Raul Rebak
On-Site Speaker (Planned) Sandipp Krishnan Ravi
Abstract Scope Iron-Chromium-Aluminum (FeCrAl) alloys are considered as lead Accident Tolerant Fuel Cladding (ATF) candidate because of their ability to form an effective passive Al film during high temperature exposure. FeCrAl alloys also exhibit good hydrothermal corrosion in light water reactor (LWR) operating conditions due to their Cr content. Though there is a significant amount of industrial data available on FeCrAl alloy behavior at high temperature environments (e.g. catalytic converter), there is a need of generating oxidation behavior data at relevant lower temperature conditions pertinent to LWR operating conditions. GE Research is conducting experiments looking at the phase stability, corrosion behavior, and mechanical properties of FeCrAl alloys with varying compositions and microstructures. A material discovery endeavor (based on alloy chemistry optimization) is undertaken through the framework of Bayesian active learning and probabilistic machine learning to develop FeCrAl alloys for LWR applications. Results from the experiments and models will be presented and discussed.

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

Automated Defect Identification for Tristructural Isotropic Fuels
B-3: 3D Computer Vision and Machine Learning for Porosity Analysis in Additive Manufacturing
Combining Limited Image and Tabular Data to Understand Failure Modes in Metals
Computer Vision Applications in Materials Science and Engineering
Establishing PSP Relationships with Microstructure Features Quantified Using Machine Learning
FeCrAl Alloy Design Utilizing Literature, Experiments, High Throughput Characterization, and Machine Learning
Machine Learning Enabled Reproducible Data Analysis for Electron Microscopy
Materials Data Science for Reliability: Data Handling
Multimodal Data of Fatigue Fracture Surfaces for Analysis in a CNN
Neighborhood Maps for Discovery of Novel Materials in Reduced Dimensions Using Machine Learning
Polycrystal Graph Neural Network
Process-Structure-Property Relationships from Variational Autoencoders

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