A mean-field approach for the determination of the polarizabilities for the water molecule in liquid state
Bibliographic record
Abstract
A mean-field method is presented describing the electrostatic environment experienced by water molecule in liquid state, which is used to extract the corresponding hyper- and high-order polarizabilities. Within this approach, MD computer simulations of liquid water samples for two standard water po tentials at several different temperatures are performed to characterize the distributions (specifically average values) of local fields and field gradients. The electric response properties (including non-linear contributions up to fourth-order) are then calculated using ab initio techniques in conjunction with a charge perturbation variant of a finite field method. Sets of fixed charges are used to generate the desired electric fields and electric field gradients. Calculations of dipole polarizability, hyper- and principal components of high-order polarizabilities of the water molecule in gas and liquid phase conditions are carried out at MP2 and MP4 levels of theory; the values obtained for three different liquid phase models are compared with those for gas phase. For a liquid phase water molecule the first hyperpolarizability (β) and first higher polarizability (A) increase markedly, actually changing sign. The second hyperpolarizability γ) also increases but much less dramatically, and components of the second high-order polarizability tensor (B) demonstrate a rearrangement of contributions. We observe that a less symmetrical gradient model gives the most accurate representation of liquid-phase conditions. The excellent agreement of our gas-phase values with experimental results and the most accurate previous theoretical predictions is evident of the quality of our higher order polarizabilities and theoretical models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".