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Pass the salt: machine learning accelerates molten salt simulations for nuclear power applications

A team of researchers from the University of Illinois Urbana-Champaign used advanced machine learning to model the physico-chemical properties of a molten salt compound called FLiNaK, enabling accurate atomic-scale reproduction and prediction of behavior under specific reactor conditions. This computational framework can help character...

SourceBeckman Institute for Advanced Science and Technology·JournalThe Journal of Physical Chemistry B·TypeComputational simulation/modeling·DateOct 11, 2021