Melting Transitions in Small Aluminum Clusters Simulated with Energies Approaching DFT Accuracy
Bibliographic record
Abstract
We describe a computational framework for modeling melting-like transitions in atomic clusters that combines first-principles energy calculations, global optimization, and machine-learned interatomic potentials. A diverse set of configurations is generated by global optimization, and Density Functional Theory calculates the associated energies. These energies are then fitted to an accuracy of 10 meV/atom or better with an Allegro E(3)-equivariant neural network potential. The resulting model allows for efficient parallel tempering Monte Carlo simulations with near-DFT-level accuracy. This methodology is validated by simulating Na 20 and comparing it to earlier experimental and computational results. Using this approach, we study melting-like transitions in Al n + clusters ( n = 9 to 16), Al n and Al n – ( n = 12, 13, 14). The simulated heat capacity of these clusters, in particular Al 16 +, is in qualitative agreement with experiments. We also observe that the melting point of Al n + clusters with n = 11–16 are well above the bulk melting point (934 K), with the closed-shell Al 13 – species possessing an exceptionally high melting point close to 2100 K.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".