About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
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| Symposium
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Cutting-Edge Characterization and Electrochemical Techniques for Unraveling Corrosion Phenomena
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| Presentation Title |
Advanced Modeling Techniques for Efficient Discovery of Corrosion Phenomena |
| Author(s) |
Ryan Katona, David Montes de Oca Zapiain, Matthew Roop, Jason Taylor, Rebecca Schaller |
| On-Site Speaker (Planned) |
Ryan Katona |
| Abstract Scope |
Corrosion is a detrimental materials degradation mechanism to many in-service systems. Investigation of corrosion often relies on ex-situ measurements post-corrosion or in-situ measurements that often influence the resultant corrosion processes. Modeling serves as a tool to probe corrosion reactions occurring throughout space and time while showing accuracy with experimental validation, also enabling determination in scenarios not testable within a lab. Herein, computational techniques that have been developed to investigate corrosion reactions will be explored. Specifically, Finite Element Method (FEM) simulations can be used to understand the operating mechanisms of corrosion propagation. Additionally, machine learning techniques also allow for the efficient exploration of FEM models as well as experimental data. This talk will cover recent advances in both FEM simulations and machine learning focusing on how these can be used to enhance our understanding of electrochemical phenomena.
SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. SAND2025-07967C. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation |