Generation and migration of helium and other point defects under irradiation causes ferritic steels based on the Fe-Cr system to age and fail. This is motivation to study point defect migration and the He equation of state using atomistic simulations due to the steels' use in future reactors. A new potential for the Fe-Cr-He system developed by collaborators at the Lawrence Livermore National Laboratory was validated using published experimental data. The results for the He equation of state agree well with experimental data. The activation energies for the migration of He- and Fe-interstitials in varying compositions of Fe-Cr lattices agree well with prior work. This research did not find a strong correlation between lattice ordering and interstitial migration energy

The irradiation of crystalline materials in environments such as nuclear reactors leads to the accumulation of micro and nano-scale defects with a negative impact on material properties such as strength, corrosion resistance, and dimensional stability. Point defects in the crystal lattice, the vacancy and self-interstitial, form the basis of this damage and are capable of migrating through the lattice to become part of defect clusters and sinks, or to annihilate themselves. Recently, attention has been given to HEAs for fusion and fission components, as some materials of this class have shown resilience to irradiation-induced damage. The ability to predict defect diffusion and accelerate simulations of defect behaviour in HEAs using ML techniques is consequently a subject that has gathered significant interest. The goal of this work was to produce an unsupervised neural network capable of learning the interatomic dynamics within a specific HEA system from MD data in order to create a KMC type predictor of defect diffusion paths for common point defects in crystal systems such as the vacancy and self-interstitial. Self-interstitial defect states were identified and purified from MD datasets using graph-isomorphism, and a proof-of-concept model for the HEA environment was used with several interaction setups to demonstrate the feasibility of training a GCN to predict vacancy defect transition rates in the HEA crystalline environment.