Long-Range Interactions in High-Dimensional Neural Network Potentials: A Benchmark Study for Small Organic Molecules
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Bibliographic record
Abstract
Many machine learning potentials (MLPs) rely on representations of the total energy in terms of the positions of the atoms in their local environment, using either a cutoff radius or a limited number of message-passing layers. This restricts their ability to model long-range intermolecular interactions accurately. This limitation can be addressed by explicitly incorporating long-range electrostatic and dispersion interactions into the MLP framework. In this paper, we investigate the impact of augmenting high-dimensional neural network potentials (HDNNPs) with both electrostatic and dispersion corrections on the prediction of gas-phase intermolecular interactions between small organic molecules. We employ a machine learning-based charge equilibration (QEq) scheme to model electrostatics and the Machine-Learning eXchange-hole Dipole-Moment (MLXDM) model to account for dispersion. The resulting model, CombineNet, integrates these long-range terms with short-range atomic energies trained on density functional theory (DFT) data and achieves a low mean absolute error (MAE) of 0.59 kcal/mol (root-mean-square error (RMSE) of 3.38 meV/atom) against CCSD(T)/CBS benchmarks on the DES370K test set. Notably, electrostatic interactions derived from Hirshfeld charges tend to underestimate long-range effects, whereas the minimal basis iterative stockholder (MBIS) charges yield more accurate interaction trends. For reliable modeling of molecular dimers, the training set must capture both the dissociation limit and the transition region near the cutoff radius.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it