NEUP CINR-19-16909: Learning-based Computational Study of the Thermodynamic, Structural, and Dynamic Properties of Molten Salts at the Atomic and Electronic Scale and Experimental Validations

The objective of this proposal is to obtain the thermophysical, thermochemical, and transport properties, construct the phase diagrams, and build empirical physical models of molten salts that are relevant to Molten Salt Reactors (MSRs) with first-principles accuracy using Molecular Dynamics (MD) simulations driven by Machine-Learned (ML) high-dimensional Neural Network Potentials (NNPs) combined with neutron/X-ray scattering and thermodynamic experimental validations.

Additional Info

Field	Value
Awarded Institution	University of Illinois at Urbana-Champaign
PI	Yang Zhang