Atomic Permutationally Invariant Polynomials for Fitting Molecular Force\n Fields
Bibliographic record
Abstract
We introduce and explore an approach for constructing force fields for small\nmolecules, which combines intuitive low body order empirical force field terms\nwith the concepts of data driven statistical fits of recent machine learned\npotentials. We bring these two key ideas together to bridge the gap between\nestablished empirical force fields that have a high degree of transferability\non the one hand, and the machine learned potentials that are systematically\nimprovable and can converge to very high accuracy, on the other. Our framework\nextends the atomic Permutationally Invariant Polynomials (aPIP) developed for\nelemental materials in [Mach. Learn.: Sci. Technol. 2019 1 015004] to molecular\nsystems. The body order decomposition allows us to keep the dimensionality of\neach term low, while the use of an iterative fitting scheme as well as\nregularisation procedures improve the extrapolation outside the training set.\nWe investigate aPIP force fields with up to generalised 4-body terms, and\nexamine the performance on a set of small organic molecules. We achieve a high\nlevel of accuracy when fitting individual molecules, comparable to those of the\nmany-body machine learned force fields. Fitted to a combined training set of\nshort linear alkanes, the accuracy of the aPIP force field still significantly\nexceeds what can be expected from classical empirical force fields, while\nretaining reasonable transferability to both configurations far from the\ntraining set and to new molecules.\n
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".