Integration of a Structural Coherence Model with a Semi-Universal Machine Learning Interatomic Potential to Predict Molten Salt Thermal Conductivity
This paper presents an integrated methodology combining SuperSalt machine-learning interatomic potentials with a structural coherence model to rapidly predict the thermal conductivity of molten salts with experimental-level accuracy at a fraction of the computational cost of traditional molecular dynamics simulations.