The exchange-correlation dipole moment dispersion method
Bibliographic record
Abstract
Density-functional theory (DFT) has become the workhorse of modern computational chemistry, with dispersion corrections such as the exchange-hole dipole moment (XDM) model playing a key role in high-accuracy modelling of large-scale systems. Here, we introduce a new physics-guided XDM variant, termed the exchange-correlation dipole moment (XCDM) model, which supplements XDM with same- and opposite-spin dynamical correlation terms, substantially improving accuracy for molecular $C_6$ dispersion coefficients. Both XDM and XCDM are implemented for use with the Becke-Johnson damping function based on atomic radii, as well as a one-parameter damping function based on atomic numbers, recently proposed by Becke. All four variants are benchmarked on the comprehensive GMTKN55 database using minimally empirical generalised-gradient-approximation, global hybrid, and range-separated hybrid functionals. This marks the first time that the XDM (and many-body dispersion, MBD) corrections have been tested for the GMTKN55 set. Five solid-state benchmarks spanning molecular crystals and layered materials are also considered. The B86bPBE0 hybrid functional, paired with any of the XDM variants, shows excellent performance for molecular systems. Finally, we identify a flaw in the weighted mean absolute deviation (WTMAD-2) scheme commonly used for the GMTKN55 set, which underweights some of its component benchmarks by orders of magnitude. We propose a new WTMAD-4 scheme based on typical errors observed for well-behaved functionals, ensuring fair treatment across all benchmarks.
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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 | 0.001 |
| 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".