A (Nearly) Universally Applicable Method for Modeling Noncovalent Interactions Using B3LYP
Bibliographic record
Abstract
B3LYP is the most widely used density-functional theory (DFT) approach because it is capable of accurately predicting molecular structures and other properties. However, B3LYP is not able to reliably model systems in which noncovalent interactions are important. Here we present a method that corrects this deficiency in B3LYP by using dispersion-correcting potentials (DCPs). DCPs are utilized by simple modifications to input files and can be used in any computational package that can read effective-core potentials. Therefore, the technique requires no programming. DCPs (developed for H, C, N, and O) produce the best results when used in conjunction with 6-31+G(2d,2p) basis sets. The B3LYP-DCP approach was tested on the S66, S22, and HSG-A benchmark sets of noncovalently interacting dimers and trimers and was found to, on average, significantly outperform almost all other DFT-based methods that were designed to treat van der Waals interactions. Users of B3LYP who wish to model systems in which noncovalent interactions (viz., steric repulsion, hydrogen bonding, π-stacking) are present, should consider B3LYP-DCP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".