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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2410.15120 |
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| _version_ | 1866917809934565376 |
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| author | Barra, Julian Shahbazi, Shayan Birri, Anthony Chahal, Rajni Isah, Ibrahim Anwar, Muhammad Nouman Starkus, Tyler Balaprakash, Prasanna Lam, Stephen |
| author_facet | Barra, Julian Shahbazi, Shayan Birri, Anthony Chahal, Rajni Isah, Ibrahim Anwar, Muhammad Nouman Starkus, Tyler Balaprakash, Prasanna Lam, Stephen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15120 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning Barra, Julian Shahbazi, Shayan Birri, Anthony Chahal, Rajni Isah, Ibrahim Anwar, Muhammad Nouman Starkus, Tyler Balaprakash, Prasanna Lam, Stephen Machine Learning Materials Science 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. |
| title | Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry-Informed Transfer Learning |
| topic | Machine Learning Materials Science |
| url | https://arxiv.org/abs/2410.15120 |