Gradient domain machine learning with composite kernels: improving the accuracy of PES and force fields for large molecules
Bibliographic record
Abstract
The generalization accuracy of machine learning models of potential energy surfaces (PES) and force fields (FF) for large polyatomic molecules can be improved either by increasing the number of training points or by improving the models.In order to build accurate models based on expensive ab initio calculations, much of recent work has focused on the latter.In particular, it has been shown that gradient domain machine learning (GDML) models produce accurate results for highdimensional molecular systems with a small number of ab initio calculations.The present work extends GDML to models with composite kernels built to maximize inference from a small number of molecular geometries.We illustrate that GDML models can be improved by increasing the complexity of underlying kernels through a greedy search algorithm using Bayesian information criterion as the model selection metric.We show that this requires including anisotropy into kernel functions and produces models with significantly smaller generalization errors.The results are presented for ethanol, uracil, malonaldehyde and aspirin.For aspirin, the model with composite kernels trained by forces at 1000 randomly sampled molecular geometries produces a global 57dimensional PES with the mean absolute accuracy 0.177 kcal/mol (61.9 cm -1 ) and FFs with the mean absolute error 0.457 kcal/mol Å -1 .
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".