SuperSalt: equivariant neural network force fields for multicomponent molten salts system
Chen Shen, Siamak Attarian, Yixuan Zhang, Hongbin Zhang, Mark Asta, Izabela Szlufarska, Dane Morgan
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. Molten salts support clean energy, but modeling their behavior is complex. Here, authors introduce SuperSalt, a machine learning potential, and SuperSalt-BO, a global optimization tool, to predict chloride melt properties and discover optimal compositions.