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Record W2897048435 · doi:10.1063/1.5050488

An efficient first principles method for molecular pump-probe NEXAFS spectra: Application to thymine and azobenzene

2018· article· en· W2897048435 on OpenAlexaff
Christopher Ehlert, Markus Gühr, Peter Saalfrank

Bibliographic record

VenueThe Journal of Chemical Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicX-ray Spectroscopy and Fluorescence Analysis
Canadian institutionsWilfrid Laurier University
FundersTechnische Universität BerlinVolkswagen FoundationDeutsche Forschungsgemeinschaft
KeywordsXANESValence (chemistry)Absorption spectroscopySpectroscopyExcited stateDensity functional theoryChemistryMolecular orbitalMoleculeThymineAtomic physicsMaterials scienceMolecular physicsComputational chemistryPhysicsOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.009
GPT teacher head0.289
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations32
Published2018
Admission routes1
Has abstractyes

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