A water potential based on multipole moments trained by machine learning — Reducing maximum energy errors
Bibliographic record
Abstract
A potential that strives to represent the Coulomb interaction realistically must include polarization. In our approach, three decisions were made to accomplish this: (i) define an atom according to quantum chemical topology (QCT), (ii) express the interaction between atoms via their multipole moments, and (iii) use machine learning to capture the response of an atomic multipole moment to a change in this atom’s environment. This approach avoids explicit (distributed) polarizabilities and eliminates the problem of polarization catastrophe. Previously, we showed ( Phys. Chem. Chem. Phys. 2009, 11, 6365 ) that a machine learning method called kriging predicted atomic multipole moments more accurately than competing machine learning methods. This was established for the atoms of a central water molecule in water clusters, from the dimer to the hexamer. The prediction errors in all multipole moments were collectively assessed by errors in total interaction energy, for thousands of clusters configurations. Here, we target the maximum errors, with an eye on reducing the worst predictions that the potential may return. We demonstrate proof-of-principle for the water dimer using local kriging.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".