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

Julian Barra; Shayan Shahbazi; Anthony Birri; Rajni Chahal; Ibrahim Isah; Muhammad Nouman Anwar; Tyler Starkus; Prasanna Balaprakash; Stephen Lam

arXiv · 2410.15120

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

Julian Barra·Shayan Shahbazi·Anthony Birri·Rajni Chahal·Ibrahim Isah·Muhammad Nouman Anwar·Tyler Starkus·Prasanna Balaprakash·Stephen Lam

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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.

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Julian Barra, Shayan Shahbazi, Anthony Birri, Rajni Chahal, Ibrahim Isah, Muhammad Nouman Anwar, Tyler Starkus, Prasanna Balaprakash, Stephen Lam. 2024-10-19. Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning. https://arxiv.org/abs/2410.15120

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