Conference Tools for 2025 TMS Annual Meeting & Exhibition

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Meeting	2025 TMS Annual Meeting & Exhibition
Symposium	Bridging Scale Gaps in Multiscale Materials Modeling in the Age of Artificial Intelligence
Presentation Title	Modelling Helium Bubble Evolution and Grain Decohesion in Nanostructured Tungsten Using ML-Based Interatomic Potential
Author(s)	Jorge Suarez Recio, Pablo Piaggi, Javier Domínguez-Gutiérrez, Raquel González Arrabal, Roberto Iglesias
On-Site Speaker (Planned)	Jorge Suarez Recio
Abstract Scope	One of the challenges facing nanostructured-tungsten (NW) as a plasma-facing material is erosion due to surface changes caused by the interaction of helium atoms with tungsten defects. This interaction forms helium bubbles and the fuzz layer, a process that is not fully understood. While MD simulations using empirical interatomic potentials often miss critical phenomena such as electronic effects at interfaces, DFT simulations, while accurate, are computationally expensive. Machine learning algorithms offer a breakthrough by generating interatomic potentials with near-DFT accuracy. We have developed an ML-based interatomic potential (MLIP) to study helium bubble behaviour at NW grain boundaries. This potential enables detailed MD simulations that provide insight into helium bubble formation and growth under irradiation. Additionally, automated defect analysis tools improve the accuracy and reliability of our results. Our work bridges the gap between empirical models and DFT, paving the way for more robust plasma-facing materials for future fusion reactors.
Proceedings Inclusion?	Planned:
Keywords	Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation

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