Harmonic transition state theory applied to vacancy diffusion pre-exponential factors in a concentrated solid-solution alloy
Bibliographic record
Abstract
High-entropy alloys are solid solutions composed of five or more elements in near-equimolar proportions that demonstrate a number of unusual properties that have yet to be fully explained. Among these, the origin and existence of diffusion qualified as sluggish have been debated since the first measurements of diffusion in disordered systems became available. To better understand the potential role of entropy in this phenomenon, we analyze vacancy diffusion in a ternary concentrated solid-solution alloy, FeNiCr. This is done through atomistic simulations using the kinetic activation-relaxation technique, an off-lattice kinetic Monte Carlo algorithm combined with an embedded atom method potential. Through an analysis of millions of activated events, we compare the kinetics of a vacancy when the activation prefactor is computed specifically within the harmonic approximation to a system where activated prefactors are set at a constant value, regardless of the environment. This allows us to identify the role of disorder on energetic barriers and prefactor distributions, particularly in the case of defect kinetics. More precisely, through the emerging statistical evidence of a compensation between the barrier and prefactor, we show that disorder strongly perturbs the system's vibrational entropy, contributing to explain sluggish diffusion.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".