Ab initio modelling of solute segregation energies to a general grain boundary
Bibliographic record
Abstract
We apply a quantum mechanical/molecular mechanical (QM/MM) multiscale approach to calculate the segregation energies of Mg and Pb to two kinds of grain boundaries in Al. The first boundary, a symmetric (310)[001] Σ 5 tilt boundary, is also tractable using traditional QM calculations, and serves as a validation for the QM/MM method. The second boundary is a general, low-symmetry tilt boundary that is completely inaccessible to pure QM calculations. QM/MM results for both of these boundaries are used to evaluate the accuracy of empirical (EAM) potentials for the Al-Mg and Al-Pb alloy systems. Based on these results we develop a physical model for the segregation energy based on elastic interaction and bond breaking terms. Both MM calculations with the EAM potentials and the model work quantitatively well for describing Mg-GB interaction across a wide range of local environments. For Pb, MM performance is weaker and the model provides only qualitative insight, demonstrating the utility of a QM/MM approach.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".