An efficient first principles method for molecular pump-probe NEXAFS spectra: Application to thymine and azobenzene
Bibliographic record
Abstract
Pump-probe near edge X-ray absorption fine structure (PP-NEXAFS) spectra of molecules offer insight into valence-excited states, even if optically dark. In PP-NEXAFS spectroscopy, the molecule is “pumped” by UV or visible light enforcing a valence excitation, followed by an X-ray “probe” exciting core electrons into (now) partially empty valence orbitals. Calculations of PP-NEXAFS have so far been done by costly, correlated wavefunction methods which are not easily applicable to medium-sized or large molecules. Here we propose an efficient, first principles method based on density functional theory in combination with the transition potential and ΔSCF methodology (TP-DFT/ΔSCF) to compute molecular ground state and PP-NEXAFS spectra. We apply the method to n → π* pump/O-K-edge NEXAFS probe spectroscopy of thymine (for which both experimental and other theoretical data exist) and to n → π* or π → π* pump/N-K-edge NEXAFS probe spectroscopies of trans- and cis-azobenzene.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".