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

arXiv: https://arxiv.org/abs/2410.15120 · HTML: https://mlsanity.com/paper/2410.15120/

## Key facts

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- First posted on arXiv 19 Oct 2024.

## Abstract (arXiv)

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 (r2 > 0.99, MAPE < 1%), outperforming the alternatives.