Influence of the Excitation Wavelength on First Order Hyperpolarizabilities and Optimal Gap Tuning of Range Separated Hybrid Functionals
Bibliographic record
Abstract
In this study, we compute the hyperpolarizability of the nitroaniline isomers, para-nitroaniline (pNA), ortho-nitroaniline (oNA), and meta-nitroaniline (mNA), by density functional theory (DFT), including with optimally tuned range separated hybrid (RSH) functionals. By utilizing the nitroanilines hyperpolarizability trend based on charge transfer (pNA>oNA>mNA), we can uncover how the excitation wavelength affects the prediction of the hyperpolarizabilities in both on and off resonant regimes, and optimal gap tuning of RSH functionals. In non-resonant regions, with reference to CCSD/aug-cc-pVDZ and experimental studies, we find that some computational approaches do not always reproduce the nitroanilines trend at specific excitation wavelengths. For example, RSH functionals require optimal gap tuning to reproduce the trend. In resonant regions, we find that the damped response theory predicts that the trend is maintained at the two-photon absorption, however, it breaks near the one photon pole. This suggests that the underlying charge transfer characteristics are undermined in the one-photon pole which in comparison to the two-state model suggests that this is due to the presence of other electronic states in some of the isomers. Furthermore, we find that cases where optimal gap tuning is ineffective (pathological behavior) are dependent on the excitation wavelength.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".