Modeling LiF and FLiBe Molten Salts with Robust Neural Network Interatomic Potentials
Bibliographic record
Abstract
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-33.3¾F 2 (Flibe) are of considerable interest in nuclear systems, as they show an excellent combination of desirable heat-transfer and neutron-absorption 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 (NNIP) provide a fast and accurate method for performing molecular dynamics of molten salts. For LiF, these potentials are able to accurately model dimer interactions, crystalline solids under deformation, semi-crystalline LiF near the melting point and liquid LiF at high temperatures. For Flibe, NNIPs accurately predicts 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 timescales (e.g., nanosecond) and large system sizes (e.g., 10 5 atoms), while maintaining ab initio accuracy and providing more than three orders of magnitude of computational speedup for calculating structure and transport properties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".