State variables for glasses: The case of amorphous ice
Bibliographic record
Abstract
Glasses are out-of-equilibrium systems whose state cannot be uniquely defined by the usual set of equilibrium state variables. Here, we seek to identify an expanded set of variables that uniquely define the state of a glass. The potential energy landscape (PEL) formalism is a useful approach within statistical mechanics to describe supercooled liquids and glasses. We use the PEL formalism and computer simulations to study the transformations between low-density amorphous ice (LDA) and high-density amorphous ice (HDA). We employ the ST2 water model, which exhibits an abrupt first-order-like phase transition from LDA to HDA, similar to that observed in experiments. We prepare a number of distinct samples of both LDA and HDA that have completely different preparation histories. We then study the evolution of these LDA and HDA samples during compression and decompression at temperatures sufficiently low that annealing is absent and also during heating. We find that the evolution of each glass sample, during compression/decompression or heating, is uniquely determined by six macroscopic properties of the initial glass sample. These six quantities consist of three conventional thermodynamic state variables, the number of molecules N, the system volume V, and the temperature T, as well as three properties of the PEL, the inherent structure (IS) energy EIS, the IS pressure PIS, and the average curvature of the PEL at the IS SIS. In other words, (N,V,T,EIS,PIS,SIS) are state variables that define the glass state in the case of amorphous ice. An interpretation of our results in terms of the PEL formalism is provided. Since the behavior of water in the glassy state is more complex than for most substances, our results suggest that these six state variables may be applicable to amorphous solids in general and that there may be situations in which fewer than six variables would be sufficient to define the state of a glass.
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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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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".