Density Functional Theory Based Model Calculations for Accurate Bond Dissociation Enthalpies. 3. A Single Approach for X−H, X−X, and X−Y (X, Y = C, N, O, S, Halogen) Bonds
Bibliographic record
Abstract
Molecule and radical enthalpies were computed using five model chemistries, which are differentiated by the method used for calculating geometries and scaled frequencies. For all the models, electronic energies were calculated using density functional theory (DFT) at the B3P86/6-311G(2d,2p) level of theory, which was selected following tests involving six hybrid functionals and three basis sets. The models were assessed for their ability to accurately predict the bond dissociation enthalpies (BDEs) of 34 X−H bonds and 28 X−X and X−Y bonds, where X, Y = C, N, O, S, and halogen. The mean absolute error (MAE) of the BDEs relative to experiment predicted using each of the five models is: AM1 = 2.1, PM3 = 1.7, HF/3-21G(d) = 1.6, B3P86/3-21G(d) = 1.4, and B3P86/6-31G(d) = 1.5 kcal/mol. The B3P86/6-311G(2d,2p)//B3P86/3-21G(d) and B3P86/6-311G(2d,2p)//B3P86/6-31G(d) models perform as well as G3(MP2) (MAE = 1.5) for the bonds in the test set and with a substantially lower computational cost. The models also perform well for Si−H bonds and for Si−X (X = C, N, O) bonds in radicals but not for Si−X bonds in closed-shell molecules. Comparisons are also made to a reparametrized version of B3LYP, which is also shown to perform well for most bonds in the test set. The models are shown to be applicable to the study of olefin line growth on silicon surfaces, an area of research in which we are currently involved. The basis set dependence of the X−H BDEs is examined. The shortcomings of the present models are discussed, with particular emphasis on the failure of various DFT methods to adequately describe molecules with extensive delocalization.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".