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About this Abstract
Meeting	2024 TMS Annual Meeting & Exhibition
Symposium	Materials Informatics to Accelerate Nuclear Materials Investigation
Presentation Title	Modeling Cascade Damage in Tungsten Using Machine Learning SNAP Interatomic Potential: Electron-Phonon Interaction Model
Author(s)	Omar Marwan Hussein, Fadi Abdeljawad, Timofey Frolov, Artur Tamm
On-Site Speaker (Planned)	Omar Marwan Hussein
Abstract Scope	Tungsten (W) is extensively studied for its potential as plasma-facing material in fusion devices due to its high melting point, excellent thermal conductivity, and low sputtering erosion. Many experiments investigated the radiation effects on W, revealing that the evolution in defect structures and microstructural transformations induce embrittlement and reduce the material's thermal conductivity. Hence, it is crucial to accurately account for the energy dissipation from atomic interactions with the electronic system, as the damage morphology significantly influences how materials respond to radiation. Herein, we use a machine learning SNAP interatomic potential to explore high-energy radiation damage processes on W, incorporating realistic electronic stopping power and electron-phonon coupling. In broad terms, our present work provides a framework to understand the primary stages of radiation effects by investigating the electron-phonon coupling effects in microstructure and damage accumulation.
Proceedings Inclusion?	Planned:
Keywords	Nuclear Materials, Modeling and Simulation, Machine Learning

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