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Record W1979382691 · doi:10.1063/1.1420401

Calculating molecular electric and magnetic properties from time-dependent density functional response theory

2002· article· en· W1979382691 on OpenAlexaff
Jochen Autschbach, Tom Ziegler

Bibliographic record

VenueThe Journal of Chemical Physics · 2002
Typearticle
Languageen
FieldChemistry
TopicMolecular spectroscopy and chirality
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMagnetic dipoleDensity functional theoryDipoleTime-dependent density functional theoryExcitationPhysicsDensity matrixRotation (mathematics)Magnetic momentElectric dipole momentPolarizabilityCondensed matter physicsQuantum electrodynamicsStatistical physicsComputational physicsQuantum mechanicsMoleculeComputer science

Abstract

fetched live from OpenAlex

Simple working equations are presented which allow for the treatment of time-dependent first-order electric and magnetic properties based on the linear response of the Kohn–Sham density matrix. They are generally applicable to existing nonrelativistic molecular density functional code implementations. This extends the formulas for frequency-dependent polarizabilities previously reported by other authors to include the cases of frequency-dependent optical rotation parameters and magnetic susceptibilities. An expression for the magnetic transition dipole moment is extracted from the results. Existing codes for excitation energies and polarizabilities can be easily extended to compute the optical rotation parameter and rotatory strengths. We further discuss possible extensions towards time-dependent current-density response.

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)0.003
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: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.194
Teacher spread0.184 · 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
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

Citations95
Published2002
Admission routes1
Has abstractyes

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