Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning

Abstract

Optimally designing molten salt applications requires knowledge of their thermophysical properties, but existing databases are incomplete, and experiments are challenging. Ideal mixing and Redlich-Kister models are computationally cheap but lack either accuracy or generality. To address this, a transfer learning approach using deep neural networks (DNNs) is proposed, combining Redlich-Kister models, experimental data, and ab initio properties. The approach predicts molten salt density with high accuracy ($r^2$ > 0.99, MAPE < 1%), outperforming the alternatives.