Predicting Energetics of Supramolecular Systems Using the XDM Dispersion Model
Bibliographic record
Abstract
In this article, we examine the ability of the exchange-hole dipole moment (XDM) model of dispersion to treat large supramolecular systems. We benchmark several XDM-corrected functionals on the S12L set proposed by Grimme, which comprises large dispersion-bound host-guest systems, for which back-corrected experimental and Quantum Monte Carlo (QMC) reference data are available. PBE-XDM coupled with the relatively economical and efficient pc-2-spd basis set gives excellent statistics (mean absolute error (MAE) = 1.5 kcal/mol), below the deviation between experimental and QMC data. When compared only to the (more accurate) QMC results, PBE-XDM/pc-2-spd (MAE = 1.2 kcal/mol) outperforms all other dispersion-corrected DFT results in the literature, including PBE-dDsC/QZ4P (6.2 kcal/mol), PBE-NL/def2-QZVP (4.7 kcal/mol), PBE-D2/def2-QZVP' (3.5 kcal/mol), PBE-D3/def2-QZVP'(2.3 kcal/mol), M06-L/def2-QZVP (1.9 kcal/mol), and PBE-MBD (1.8 kcal/mol), with no significant bias (mean error (ME) = 0.04 kcal/mol). PBE-XDM/pc-2-spd gives binding energies relatively close to the complete basis-set limit and does not necessitate the use of counterpoise corrections, which facilitates its use. The dipole-quadrupole and quadrupole-quadrupole pairwise dispersion terms (C8 and C10) are critical for the correct description of the dimers. XDM-corrected functionals different from PBE that work well for small dimers do not yield good accuracy for the large supramolecular systems in the S12L, presenting errors that scale linearly with the dispersion contribution to the binding energy.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".