Automated fit of high-dimensional potential energy surfaces using cluster analysis and interpolation over descriptors of chemical environment
Bibliographic record
Abstract
We present a method for fitting high-dimensional potential energy surfaces that is almost fully automated, can be applied to systems with various chemical compositions, and involves no particular choice of function form. We tested it on four systems: Ag20, Sn6Pb6, Si10, and Li8. The cost for energy evaluation is smaller than the cost of a density functional theory (DFT) energy evaluation by a factor of 1500 for Li8, and 60,000 for Ag20. We achieved intermediate accuracy (errors of 0.4 to 0.8 eV on atomization energies, or, 1% to 3% on cohesive energies) with rather small datasets (between 240 and 1400 configurations). We demonstrate that this accuracy is sufficient to correctly screen the configurations with lowest DFT energy, making this function potentially very useful in a hybrid global optimization strategy. We show that, as expected, the accuracy of the function improves with an increase in the size of the fitting dataset.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".