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About this Abstract
Meeting	2024 TMS Annual Meeting & Exhibition
Symposium	Materials Informatics to Accelerate Nuclear Materials Investigation
Presentation Title	Machine Learning Enhanced Kinetic Monte Carlo Modeling of Molten Salt Corrosion of Ni-Cr Alloys
Author(s)	Jilang Miao, Miaomiao Jin, Hamdy Arkoub
On-Site Speaker (Planned)	Jilang Miao
Abstract Scope	Understanding the corrosion behavior between the Ni-Cr structural alloys and the molten fluoride salt is of significance to evaluate the performance of molten salt systems. The surface microstructural evolution is driven by complex surface processes including surface diffusion, Cr dissolution, and salt adsorption. In this work, we apply lattice kinetic Monte Carlo (KMC) to understand the (sub)surface evolution. However, a major bottleneck of KMC is the energy barriers of atomic events as they are highly dependent on the local atomic environment. To increase the accuracy of KMC, we combine machine learning with KMC where the energy barriers are predicted by the machine learning model. The time-evolving surface reconstruction is obtained, and an effective Cr dissolution rate is deduced considering the variations of surface condition and alloy composition. These results provide useful information for evaluating the local corrosion of Ni-based alloys in molten salt for the advancement of molten salt reactors.
Proceedings Inclusion?	Planned:

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