Bond-Valence Constraints on Liquid Water Structure
Bibliographic record
Abstract
The recent controversy about the structure of liquid water pits a new model involving water molecules in relatively stable "rings-and-chains" structures against the standard model that posits water molecules in distorted tetrahedral coordination. Molecular dynamics (MD) simulations, both classical and ab initio, almost uniformly support the standard model, but because none of them can yet reproduce all of the anomalous properties of water, they leave room for doubt. We argue that it is possible to evaluate these simulations by testing them against their adherence to the bond-valence model, a well-known and quantitatively accurate empirical summary of the behavior of atoms in the bonded networks of inorganic solids. Here we use the results of ab initio MD simulations of ice, water, and several solvated aqueous species to show that the valence sum rule (the first axiom of the bond-valence model) is followed in both solid and liquid bond networks. We then test MD simulations of water, employing several popular potential models against this criterion and the experimental O-O RDF. It appears that most of those tested cannot satisfy both criteria well, except TIP4P, TIP4P/2005, and TIP5P. If the valence sum rule really can be applied to simulated liquid structures, then it follows that the bonding behaviors of atoms in liquids are in some ways identical to those in solids. We support this interpretation by showing that the simulations produce O-H...O geometries that are completely consistent with the range of geometries available in solids, and the distributions of instantaneous valence sums reaching the atoms in both the ice and liquid water simulations are essentially identical. Furthermore, we show that none of the extant asymmetric water potentials that produce "rings-and-chains" structures can satisfy our geometric criteria. Taken together, this is powerful evidence in favor of the standard distorted tetrahedral model of liquid water structure.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".