Studies of Ni-Cr complexation in FLiBe molten salt using machine learning interatomic potentials
Siamak Attarian, Dane Morgan, Izabela Szlufarska

TL;DR
This study uses machine learning interatomic potentials to investigate impurity complexation in molten salt FLiBe, revealing weak binding energies and potential impacts on redox behavior relevant to nuclear and solar applications.
Contribution
It introduces a machine learning-based approach to model impurity complexation in molten salts, overcoming sampling challenges with active learning for accurate long-term simulations.
Findings
Weak binding energy between CrF2 and NiF2 (-0.112 eV)
Complexation influences redox potential and short-range order
Little effect on diffusivity of Ni and Cr
Abstract
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…
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