Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potential
Lithium-based molten salts have attracted significant attention due to their applications in energy storage, advanced fission reactors, and fusion devices. Lithium fluorides and particularly 66.6%LiF-33.3%BeF2 (Flibe) are of considerable interest in nuclear systems, as they show an excellent combina...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Chemical Society (ACS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/135621 |
| _version_ | 1811093167584313344 |
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| author | Lam, Stephen T Li, Qing-Jie Ballinger, Ronald Forsberg, Charles Li, Ju |
| author_facet | Lam, Stephen T Li, Qing-Jie Ballinger, Ronald Forsberg, Charles Li, Ju |
| author_sort | Lam, Stephen T |
| collection | MIT |
| description | Lithium-based molten salts have attracted significant attention due to their applications in energy storage, advanced fission reactors, and fusion devices. Lithium fluorides and particularly 66.6%LiF-33.3%BeF2 (Flibe) are of considerable interest in nuclear systems, as they show an excellent combination of favorable heat transfer, neutron moderation, and transmutation characteristics. For nuclear salts, the range of possible local structures, compositions, and thermodynamic conditions presents significant challenges in atomistic modeling. In this work, we demonstrate that atom-centered neural network interatomic potentials (NNIPs) provide a fast method for performing molecular dynamics of molten salts that is as accurate as ab initio molecular dynamics. For LiF, these potentials are able to accurately reproduce ab initio interactions of dimers, crystalline solids under deformation, crystalline LiF near the melting point, and liquid LiF at high temperatures. For Flibe, NNIPs accurately predict the structures and dynamics at normal operating conditions, high-temperature-pressure conditions, and in the crystalline solid phase. Furthermore, we show that NNIP-based molecular dynamics of molten salts are scalable to reach long time scales (e.g., nanosecond) and large system sizes (e.g., 105 atoms) while maintaining ab initio density functional theory accuracy and providing more than 3 orders of magnitude of computational speedup for calculating structure and transport properties. |
| first_indexed | 2024-09-23T15:40:44Z |
| format | Article |
| id | mit-1721.1/135621 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T15:40:44Z |
| publishDate | 2021 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/1356212021-10-28T03:21:05Z Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potential Lam, Stephen T Li, Qing-Jie Ballinger, Ronald Forsberg, Charles Li, Ju Lithium-based molten salts have attracted significant attention due to their applications in energy storage, advanced fission reactors, and fusion devices. Lithium fluorides and particularly 66.6%LiF-33.3%BeF2 (Flibe) are of considerable interest in nuclear systems, as they show an excellent combination of favorable heat transfer, neutron moderation, and transmutation characteristics. For nuclear salts, the range of possible local structures, compositions, and thermodynamic conditions presents significant challenges in atomistic modeling. In this work, we demonstrate that atom-centered neural network interatomic potentials (NNIPs) provide a fast method for performing molecular dynamics of molten salts that is as accurate as ab initio molecular dynamics. For LiF, these potentials are able to accurately reproduce ab initio interactions of dimers, crystalline solids under deformation, crystalline LiF near the melting point, and liquid LiF at high temperatures. For Flibe, NNIPs accurately predict the structures and dynamics at normal operating conditions, high-temperature-pressure conditions, and in the crystalline solid phase. Furthermore, we show that NNIP-based molecular dynamics of molten salts are scalable to reach long time scales (e.g., nanosecond) and large system sizes (e.g., 105 atoms) while maintaining ab initio density functional theory accuracy and providing more than 3 orders of magnitude of computational speedup for calculating structure and transport properties. 2021-10-27T20:24:18Z 2021-10-27T20:24:18Z 2021 2021-08-12T18:04:55Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/135621 en 10.1021/acsami.1c00604 ACS Applied Materials & Interfaces Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf American Chemical Society (ACS) ACS |
| spellingShingle | Lam, Stephen T Li, Qing-Jie Ballinger, Ronald Forsberg, Charles Li, Ju Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potential |
| title | Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potential |
| title_full | Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potential |
| title_fullStr | Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potential |
| title_full_unstemmed | Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potential |
| title_short | Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potential |
| title_sort | modeling lif and flibe molten salts with robust neural network interatomic potential |
| url | https://hdl.handle.net/1721.1/135621 |
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