Benchmarking semiempirical, Hartree–Fock, DFT, and MP2 methods against the ionization energies and electron affinities of short- through long-chain [<i>n</i>]acenes and [<i>n</i>]phenacenes
Bibliographic record
Abstract
Vertical and adiabatic ionization energies (IEs) and electron affinities (EAs) were calculated for the n = 1–10 [n]acenes using a wide range of semiempirical, Hartree–Fock, density functional, and second-order Moller–Plesset perturbation theory model chemistries. None of the model chemistries examined was able to accurately predict the IEs or EAs for both short- through long-chain [n]acenes, as well as for extrapolations to the polymeric limit, when compared to available experimental and benchmark theoretical data. Except for 6-31G(d), the choice of the basis set does not affect B3LYP results significantly. Analogous calculations using a suite of eight modern and (or) popular density functionals for the n = 1–10 [n]phenacenes revealed similar problems in estimating the IEs and EAs of these compounds, with the sole exception of the M062X functional for adiabatic IEs and potentially the APFD, B3LYP, and MN12SX functionals for adiabatic EAs. The poor IE/EA prediction performance for the parent [n]acenes and [n]phenacenes may extend to their substituted derivatives and heteroatom-substituted analogs. Consequently, caution should be exercised in the application of non-high-level calculations for estimating the IE/EA of these important classes of materials.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".