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Record W1988016696 · doi:10.1002/qua.1540

Properties of atoms in crystals: Dielectric polarization

2001· article· en· W1988016696 on OpenAlexaff
Richard F. W. Bader, Chérif F. Matta

Bibliographic record

VenueInternational Journal of Quantum Chemistry · 2001
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDielectricPolarization (electrochemistry)PhysicsFeynman diagramQuantumQuantum mechanicsBounded functionChemistryAtomic physicsCondensed matter physicsMathematics

Abstract

fetched live from OpenAlex

Abstract It is shown that the polarization of a molecule or an extended system, permanent or induced can, like all measurable properties, be equated to a sum of atomic contributions. While it has been previously shown that a change in the polarization of a dielectric can be considered a consequence of a geometric quantum phase and obtainable from a Berry phase in a parameter space, such a possibility does not exclude a real space description, stated in terms of the charge distributions of the system's composite atoms or cells. The cells are defined as bounded regions of real space whose properties are described by the physics of a proper open system, a description that applies to any system regardless of the nature of the interactions between the atoms. This approach necessarily leads to the inclusion of a contribution to the polarization arising from the transfer of charge across the boundary of a cell, in addition to that from the cell's internal polarization, thereby correcting the textbook description of polarization that considers only the latter contribution. It is shown that a neutral repeating cell in a dielectric behaves as an atomic capacitor which mimics the macroscopic behavior, with the internal charge transfer leading to the creation of what Feynman terms the “surface polarization charge.” © 2001 John Wiley & Sons, Inc. Int J Quantum Chem, 2001

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.262
Teacher spread0.249 · 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 teacher head, 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

Citations47
Published2001
Admission routes1
Has abstractyes

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