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Reconstructing Molecular Orbitals with Laser-Induced Electron Tunneling Spectroscopy

2023· article· en· W4389305937 on OpenAlexaff
XuanYang Lai, RenPing Sun, ShaoGang Yu, YanLan Wang, Wei Quan, A. Staudte, Xiaojun Liu

Bibliographic record

VenueUltrafast Science · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsJoint Attosecond Science Laboratory
FundersScience and Technology Department of Hubei ProvinceChinese Academy of SciencesNational Natural Science Foundation of China
KeywordsMolecular orbitalMolecular orbital theoryAtomic orbitalAtomic physicsX-ray photoelectron spectroscopyElectronMolecular physicsSpectroscopyChemistryPhysicsMoleculeNuclear magnetic resonanceQuantum mechanics

Abstract

fetched live from OpenAlex

Photoelectron spectroscopy in intense laser fields has proven to be a powerful tool for providing detailed insights into molecular structure. The ionizing molecular orbital, however, has not been reconstructed from the photoelectron spectra, because its phase information is difficult to access. Here, we propose a method to retrieve the phase information of the ionizing molecular orbital. By analyzing the interference pattern in the photoelectron spectrum, the weighted coefficients and the relative phases of the constituent atomic orbitals for a molecular orbital can be extracted. With this information, we reconstruct the highest occupied molecular orbital of N 2 . Our work provides a reliable and straightforward approach for reconstructing molecular orbitals with the photoelectron spectroscopy.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.272
Teacher spread0.261 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations8
Published2023
Admission routes1
Has abstractyes

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