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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 source (direct Gemma or distilled Codex), 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".