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Record W7047169611

Exterior Complex Scaling Approach for Atoms and Molecules in Strong DC Fields

2021· other· en· W7047169611 on OpenAlexaff

Bibliographic record

VenueYork University Digital Library (York University) · 2021
Typeother
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsYork University
Fundersnot available
KeywordsScalingHydrogen atomResonance (particle physics)IonizationContext (archaeology)QuantumValence (chemistry)Quantum field theorySchrödinger equation
DOInot available

Abstract

fetched live from OpenAlex

We perform a short review of the history of quantum mechanics, with a focus on the historical problems with describing ionization theoretically in the context of quantum mechanics. The essentials of the theory of resonances are presented. The exterior complex scaling method for obtaining resonance parameters within the context of the Schrodinger equation is detailed. We explain how this is implemented for a numerical solution using a finite element method for the scaled variable. Results for the resonance parameters of a one-dimensional hydrogen model in an external direct current (DC) electric field are presented as proof of the independence of the theory from the scaling angle. We apply the theory to the real hydrogen atom in a DC field and present results which agree with literature values. The resonance parameters for singly ionized helium are also presented. Using a model potential energy for the water molecule, we solve for the energy eigenvalues. We then solve for the resonance parameters of the water molecule in a DC field and compare to literature results. Our widths for the valence orbital are shown to agree well with the so-called "coupled-cluster singles and doubles with perturbative triples excitations" method.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.020
GPT teacher head0.190
Teacher spread0.170 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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