SuperSalt: equivariant neural network force fields for multicomponent molten salts system

Chen Shen (), Siamak Attarian, Yixuan Zhang, Hongbin Zhang, Mark Asta, Izabela Szlufarska and Dane Morgan ()
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Chen Shen: University of Wisconsin-Madison
Siamak Attarian: University of Wisconsin-Madison
Yixuan Zhang: Technical University of Darmstadt
Hongbin Zhang: Technical University of Darmstadt
Mark Asta: University of California
Izabela Szlufarska: University of Wisconsin-Madison
Dane Morgan: University of Wisconsin-Madison

Nature Communications, 2025, vol. 16, issue 1, 1-13

Abstract: Abstract Molten salts are crucial for clean energy applications, yet exploring their thermophysical properties across diverse chemical space remains challenging. We present the development of a machine learning interatomic potential (MLIP) called SuperSalt, which targets 11-cation chloride melts and captures the essential physics of molten salts with near-DFT accuracy. Using an efficient workflow that integrates systems of one, two, and 11 components, the SuperSalt potential can accurately predict thermophysical properties such as density, bulk modulus, thermal expansion, and heat capacity. Our model is validated across a broad chemical space, demonstrating excellent transferability. We further illustrate how Bayesian optimization combined with SuperSalt can accelerate the discovery of optimal salt compositions with desired properties. This work provides a foundation for future studies that allows easy extensions to more complex systems, such as those containing additional elements.

Date: 2025
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DOI: 10.1038/s41467-025-62450-1

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