A new approach to solid-state nucleation in kinetically-constrained systems
Bibliographic record
Abstract
Nucleation is the first step of the phase transformations that we use to control the microstructures of engineering materials. The starting point for questions of nucleation is usually Classical Nucleation Theory (CNT) but for solid-state nucleation at low temperatures where atomic mobility is limited, such as in engineering alloys, CNT has not been very successful is quantitatively predicting nucleation. A strong assumption of CNT is that all thermally-induced stochastic fluctuations, no matter how far their compositions lie from the bulk alloy composition, are possible and that they become nuclei when a critical size determined from thermodynamics is reached. Here we present a new and complementary model for solid-state nucleation. We consider the other extreme where atomic mobility is limited and thermally-induced stochastic clusters cannot form in the time scale relevant for a nucleation event. Instead, we consider the geometric clusters that are a statistical feature of any solution as the origin of the nuclei and present a simple model for the number of nuclei and their rate of ‘activation’. This new ‘geometric cluster’ model is shown to be able to successfully predict the competition in phase nucleation during the crystallization of a series of Al-Ni-Y metallic glass, predict the solvent trapping that is increasingly seen in solid-state nucleation and predict the peak number density of precipitates observed in Cu-Co and Fe-Cu alloys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".