An international team of researchers led by University of Wisconsin-Madison materials engineers has developed a machine learning-based tool called “SuperSalt” that accurately simulates and predicts the properties of molten salt systems. The tool will help researchers tailor molten salts for emerging energy storage applications and the harsh environments of next-generation nuclear reactors.
The research, led by UW-Madison materials science and engineering postdoctoral scholars Chen Shen and Siamak Attarian, appeared August 7, 2025, in the journal Nature Communications.
“Molten salt is very important for clean energy and nuclear reactors, but it’s very hard for experimentalists to get a deep understanding of its physical properties,” says Shen. “So we used machine learning to generate a semi-universal potential to understand multi-component salt systems.”
Read the full story here: https://engineering.wisc.edu/news/machine-learning-tool-accelerates-molten-salt-design-for-next-gen-energy-systems/