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Record W2045266829 · doi:10.1002/jcc.10131

Determination of the effective dielectric constant from the accurate solution of the Poisson equation

2002· article· en· W2045266829 on OpenAlexafffund
Vladislav Vasilyev

Bibliographic record

VenueJournal of Computational Chemistry · 2002
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsBiotechnology Research Institute
FundersNational Research Council Canada
KeywordsDielectricConstant (computer programming)Poisson's equationPoisson distributionFunction (biology)Range (aeronautics)Work (physics)Simple (philosophy)ChemistryMoleculePhysicsStatistical physicsMathematicsQuantum mechanicsComputational chemistryMaterials scienceStatisticsComputer science

Abstract

fetched live from OpenAlex

Constant dielectric (CD) and distance-dependent dielectric (DDD) functions are the most popular and widespread in the Molecular Mechanics simulations of large molecular systems. In this article, we present a simple procedure to derive an effective dielectric constant, epsilon (out,eff), for these two methods based on numerical solutions of the Poisson equation. It was found that because of the very approximate nature of the CD and DDD models there is no universal epsilon (out,eff), which will work equally well for all molecular systems. For example, different MD trajectories of the same molecule can produce different optimal epsilon (out,eff)s. The DDD function was found to yield better agreement with the numerical solutions of the Poisson equation than a CD model does. The reason is that a DDD function gives a better description of the electrostatic interactions at short distances between the atoms. Another interesting finding of this study is that under certain conditions epsilon (out,eff) can take negative values for a system of two atoms at a limited distance range. However, in principle, there is nothing to prevent the epsilon (out,eff) from taking negative values for specific conformations of some molecules.

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 score0.378
Threshold uncertainty score0.166

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 head0.012
GPT teacher head0.243
Teacher spread0.231 · 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

Citations11
Published2002
Admission routes2
Has abstractyes

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