Accurate Modeling of Water Clusters with Density-Functional Theory Using Atom-Centered Potentials
Bibliographic record
Abstract
The ability of atom-centered potentials (ACPs) to improve the modeling of water clusters using density-functional methods is explored. Water-specific ACPs were developed using accurate ab initio reference data to correct the deficiencies of the BHandHLYP density functional in the calculation of absolute and relative binding energies of water clusters. In conjunction with aug-cc-pVTZ basis sets and with or without dispersion corrections, it is possible to obtain absolute binding energies for water clusters containing up to 10 H 2 O molecules to within 0.44 kcal/mol or 0.04 kcal/mol per water molecule. In contrast, dispersion-corrected BHandHLYP/aug-cc-pVTZ predicts binding energies with errors as large as 6 kcal/mol for (H 2 O) 10 in the absence of ACPs. Therefore, the ACPs improve predicted binding energies in these clusters by more than an order of magnitude. The conformers of (H 2 O) 16 and (H 2 O) 17 were used to validate the application of ACPs to larger clusters. ACP-based approaches are able to predict the binding energies in (H 2 O) 16,17 within a range of 0.3–2.2 kcal/mol (less than 1.3%) of recently revised ab initio wave function results. ACPs for basis sets smaller than aug-cc-pVTZ are also presented. However, the ability of the BHandHLYP/ACP approach to predict accurate binding energies deteriorates as the size of the basis sets decreases. Nevertheless, ACPs improve predicted binding energies by as much as a factor of 50 across the range of the basis sets studied. The BHandHLYP/aug-cc-pVTZ-ACP method was applied to (H 2 O) 25 in order to identify the minimum-energy structure of a collection of proposed global minimum-energy structures. The BHandHLYP/aug-cc-pVTZ-ACP approach is an accurate and computationally affordable alternative to wave function theory methods for the prediction of the binding energies and energy ranking of water clusters.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".