Symmetry-adapted models for the hyperpolarizability of water
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Bibliographic record
Abstract
Abstract Accurately modeling nonlinear optical experiments such as second-harmonic scattering and hyper-Raman spectroscopy requires the hyperpolarizability β , a nonlinear response to an applied electric field. The hyperpolarizability tensor is a computationally expensive quantity to calculate, making it a natural target for machine-learning methods. We test a family of recently developed models for the hyperpolarizability of water, trained on small clusters containing up to 8 water molecules. These models are able to predict β for larger clusters, with more complex structures than those observed in the training set. For configurations of bulk water, the agreement is not so straightforward: while the total hyperpolarizability is quite well described, the predicted molecular β tensors vary wildly between models. This means that while experiments whose outputs depend on total hyperpolarizability can be accurately modeled, those that require molecular quantities will require improved models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it