  In nuclear and/or solar applications that involve molten salts, impurities
frequently enter the salt as either fission products or via corrosion.
Impurities can interact and make complexes, but the impact of such complexation
on the properties of the salts and corrosion rates has not been understood.
Common impurities in molten salts, such as FLiBe, include Cr, Ni, and Fe. Here,
we investigate the complexation of Cr and Ni in FLiBe using molecular dynamics
based on a machine learning interatomic potential (MLIP) fitted using the
atomic cluster expansion (ACE) method. The MLIP allows us to overcome the
challenges of simultaneously needing accurate energetics and long time scale to
study complexation. We demonstrate that impurity behavior is more difficult to
capture than that of concentrated elements with MLIPs due to less sampling in
training data, but that this can be overcome by using active learning
strategies to obtain a robust fit. Our findings suggest that there is a weak
but potentially significant binding free energy between CrF2 and NiF2 in
eutectic FLiBe of -0.112 eV. Under certain conditions this binding creates a
significant short-range order between the species and lowers the redox
potential of NiF2 in the presence of CrF2 in FLiBe, making Ni dissolution more
favorable in the presence of Cr as compared to its dissolution in pure FLiBe.
However, we find little impact of this complexation on the diffusivity of Ni
and Cr. Overall, the methodology presented here suggests an approach to
modeling complexation with MLIPs and suggests that interactions between
dissolved cations could be playing a significant role in some salt
thermophysical properties.
