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

Julian Barra, Shayan Shahbazi, Anthony Birri, Rajni Chahal, Ibrahim Isah +4 more
10/19/2024
cs.LGcond-mat.mtrl-sci

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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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Cite this paper

@article{barra2024generalizable,
  title  = {Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning},
  author = {Julian Barra and Shayan Shahbazi and Anthony Birri and Rajni Chahal and Ibrahim Isah and Muhammad Nouman Anwar and Tyler Starkus and Prasanna Balaprakash and Stephen Lam},
  year   = {2024},
  eprint = {2410.15120},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2410.15120v1}
}

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