Search for: All records

For the development and optimization of molten salt reactors, nuclear fuel cycles, and energy storage materials, the temperature-dependent molten salt properties must be known over a wide range of possible compositions. Yet, for several relevant chloride and fluoride salt systems, significant gaps in data still exist, which inhibit the development of key advanced energy technologies. [1] Filling all of these gaps with high-temperature experiments is inherently challenging, especially due to the corrosive, volatile, and hazardous nature of these salts. Meanwhile, carefully validated atomistic simulations (ab initio, classical or machine learning-based) are capable of predicting thermophysical properties but are highly computationally expensive [2-5], limiting our ability to screen over large temperature-compositional spaces. In this work, we propose to circumvent these limitations by using supervised machine learning (ML) models to learn from existing bulk density data and predict densities of new and unseen mixtures.