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Record W3130716649 · doi:10.1063/5.0047642

Simple pair-potentials and pseudo-potentials for warm-dense matter applications

2021· article· en· W3130716649 on OpenAlexaff

Bibliographic record

VenuePhysics of Plasmas · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsDensity functional theorySimple (philosophy)Warm dense matterIonTetrahedronMolecular dynamicsClass (philosophy)Electron

Abstract

fetched live from OpenAlex

We present computationally simple parameter-free pair potentials useful for solids, liquids, and plasmas at arbitrary temperatures. They successfully treat warm-dense matter (WDM) systems like carbon or silicon with complex tetrahedral or other structural bonding features. Density functional theory asserts that only one-body electron densities and one-body ion densities are needed for a complete description of electron–ion systems. Density functional theory (DFT) is used here to reduce both the electron many-body problem and the ion many-body problem to an exact one-body problem, namely, that of the neutral pseudoatom (NPA). We compare the Stillinger–Weber (SW) class of multi-center potentials, the embedded-atom approaches, and N-atom DFT, with the one-atom DFT approach of the NPA to show that many-ion effects are systematically included in this one-center method via one-body exchange-correlation functionals. This computationally highly efficient one-center DFT-NPA approach is contrasted with the usual N-center DFT calculations that are coupled with molecular dynamics simulations to equilibrate the ion distribution. Comparisons are given with the pair-potential parts of the SW, “glue” models, and the corresponding NPA pair-potentials to elucidate how the NPA potentials capture many-center effects using single-center one-body densities.

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 score0.247
Threshold uncertainty score0.725

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)0.0010.000

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 head0.013
GPT teacher head0.224
Teacher spread0.211 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

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