Development of MLIP to model corrosion behavior in Molten Salt Reactors

In this study, we developed and evaluated the efficacy of Machine Learning Interatomic Potentials (MLIP) designed for Molten Salt and its relevance toward the corrosion behavior. We implemented a number of methodologies and Machine Learning (ML) codes to develop the potentials, ranging the Moment Tensor Potentials (MTP), Invariant-based Deep Learning Potentials to the Equavariant-based Neural Network Potentials. We optimized the hyperparameters to account for the initial corrosion mechanisms and the cluster dynamics within the molten salts. We then compare our results with the experimental observations pertaining to, for example, de-alloying mechanisms in Ni-based alloys observed under corrosive environments.