POSTDOCTORAL ASSOCIATE, Nuclear Reactor Lab, to work with Dr. Boris Khaykovich and Prof. Ju Li to analyze experimental data and use it to build and test atomistic structural and dynamics models with the help of artificial intelligence. Molten salts are widely used for electrical and thermal energy storage, materials processing, and in Generation-IV molten-salt fission reactors (MSR) and future fusion reactors (ARC) as coolant and/or fuel. In particular, the MSR application requires detailed knowledge of the molten salt properties in order to understand and predict the salt’s behavior in service. Fundamental properties of interest include molecular structure and speciation, as well as dynamic properties such as diffusion coefficients for the salt and corrosion and fission products dissolved in it. Computer modeling is necessary to predict changes in physical and chemical properties due to irradiation and burning of dissolved fuel. The group will perform ab initio molecular dynamics simulations to understand the multi-component liquid solution. Machine learning algorithms will be used to develop a fast-acting model that can handle molten salt with an arbitrary number of chemical elements and be able to predict chemical potential as a function of composition and temperature. The measurement data for the modeling will be obtained with the help of advanced neutron and x-ray scattering and spectroscopy, which provide the most reliable and direct determination of the structure and dynamics of the melts.
REQUIRED: interest and experience in ab initio simulations of materials, machine learning, and/or data processing and evaluation of neutron and X-ray diffraction measurements. Background in chemistry, physics, chemical engineering, and nuclear engineering preferred.
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