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

• URL: http://arxiv.org/abs/2410.15120v1
• Date: Sat, 19 Oct 2024 14:28:46 GMT
• Title: Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning
• Authors: Julian Barra, Shayan Shahbazi, Anthony Birri, Rajni Chahal, Ibrahim Isah, Muhammad Nouman Anwar, Tyler Starkus, Prasanna Balaprakash, Stephen Lam,
• Abstract summary: Optimally designing molten salt applications requires knowledge of their thermophysical properties. 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.
• Score: 2.251726366940184
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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