Conference Tools for 2024 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2024 TMS Annual Meeting & Exhibition
Symposium	Materials Informatics to Accelerate Nuclear Materials Investigation
Presentation Title	Probing Radiation Induced Interface Metastability Using Deep Learning Object Detection
Author(s)	Emily M. Hopkins, Sicong He, Annie Barnett, Michael Falk, Jaime Marian, Mitra Taheri
On-Site Speaker (Planned)	Emily M. Hopkins
Abstract Scope	Achieving radiation tolerance in crystalline materials will require a thorough understanding of defect evolution and corresponding material responses to ion bombardment. Tailoring grain boundaries to behave as enhanced defect sinks poses a potential solution toward the development of more radiation tolerant materials; however, critical nuances illustrating the microstructural response of grain boundaries under irradiation have yet to be explained. In particular, the relationship between GB structural states and their effect on the rate of defect absorption is unclear. In this study, we utilize automated object detection models of in situ TEM experiments to offer insight into transient GB states and analyze state transitions with rate theory models and molecular dynamics simulations. By providing an indicator of changes in GB absorption mechanisms, we move closer to explaining GB metastability with the onset of radiation damage and the deviation of interfacial responses in pure and compositionally complex materials.
Proceedings Inclusion?	Planned:

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

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Materials Genomics Search for Helium-absorbing Nano-phases in Fusion Structural Materials

Modeling Cascade Damage in Tungsten Using Machine Learning SNAP Interatomic Potential: Electron-Phonon Interaction Model

Neural Networks of Defect Kinetics in Refractory Alloys

Optimizing Thermal Conductivity Prediction of Uranium Compounds using Balanced Multiclass Classification

Probing Radiation Induced Interface Metastability Using Deep Learning Object Detection

Putting Artificial Intelligence into Action to Quantify Radiation Effects in Materials

Quantitative Insight to Fission Gas Bubble Distribution and Lanthanide Movement in Irradiated Annular U-10Zr Metallic Fuel Using Deep Learning

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