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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium Modeling, AI Applications, and Method Development in Nuclear Materials
Presentation Title Defects and Impurity Properties of VN Precipitates in ARAFM Steels: Modelling using a Universal Machine Learning Potential and Experimental Validation
Author(s) Ryan Shaun Stroud, Colleen Reynolds, Tom Melichar, Jack Haley, Megan Carter, Michael Moody, Chris Hardie, David Bowden, Duc Nguyen-manh, Mark Wenman
On-Site Speaker (Planned) Ryan Shaun Stroud
Abstract Scope VN precipitates used to strengthen ARAFM steels for fusion applications, have been shown to undergo dissolution under high Fe ion irradiation doses of 100 dpa at dose rates of 10−3 dpa · s−1 at 600 oC. Here, point defects and solute substitutions have been studied using atom probe tomography (APT), universal machine learning interatomic potentials (uMLIPs), and density functional theory. Through a combination of transmission electron microscopy, APT, and atomic scale calculations, N-vacancies and substitutional Cr are found to be present in VN precipitates prior to irradiation. Lattice based Monte Carlo and collision cascades confirm both the presence of ordered vacancies at targeted operating temperatures and the role of vacancies in mitigating irradiation damage. The addition of solutes into VN resulted in the disruption of vacancy ordering and enhanced irradiation induced damage possibly accelerating dissolution.
Proceedings Inclusion? Planned:
Keywords Nuclear Materials, Modeling and Simulation, Machine Learning

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