Free energy simulations with machine learning-based forcefields for prediction of thermodynamic properties of molten salts

The high specific heat capacity and low volatility of molten salts makes them excellent candidates for many high temperature applications, such as molten salt nuclear reactors and concentrated solar power plants. However, the design and optimization of molten salts and their mixtures for targeted applications is largely hindered without cost-effective approaches for quickly characterizing candidates from the vast compositional space available. For example, the ability to computationally predict chemical potentials would grant insight into many chemical properties that are pertinent to molten salt applications, such as salt basicity, solubilities, phase boundaries, and redox potentials. However, the high computational cost of quantum chemical methods, such as density functional theory (DFT), limits the use of advanced techniques, such as thermodynamic integration (TI), for rapidly computing chemical potentials. To address this issue, we have utilized machine learning-based forcefields (MLFFs) that are trained to accurately reproduce DFT energies, forces, and stresses at a fraction of the cost, thereby enabling TI calculations. In this talk, we will describe several methods we have tested for computing the excess chemical potential of molten lithium chloride using TI and MLFFs. We will employ the most robust methodology for predicting redox potentials of several transition metals in molten lithium chloride and mixing free energies in the LiCl-CsCl system. This work showcases the key advantages and unique challenges of leveraging MLFFs in thermodynamic property predictions and lays the foundation for the rapid characterization of molten salt systems.