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Record W2512852012 · doi:10.1103/physrevb.94.104107

Role of quantum ion dynamics in the melting of lithium

2016· article· en· W2512852012 on OpenAlexafffund
Sabri Elatresh, Stanimir Bonev, Eugene Gregoryanz, N. W. Ashcroft

Bibliographic record

VenuePhysical review. B./Physical review. B · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicHigh-pressure geophysics and materials
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaLawrence Livermore National LaboratoryEngineering and Physical Sciences Research CouncilU.S. Department of EnergyNational Science Foundation
KeywordsIonQuantumLithium (medication)Materials scienceMelting curve analysisDynamics (music)ThermodynamicsCondensed matter physicsPhysicsChemistryQuantum mechanics

Abstract

fetched live from OpenAlex

The role of quantum ion dynamics in the low melting temperatures of Li is investigated from first principles theory. Free energies of solid and liquid phases are obtained at the classical and quantum ion levels. The results are used to determine the Li melting curve in the 40--60 and 110--150 GPa pressure ranges and are in excellent agreement with experimental data around 50 GPa. They predict the resumption of a positive melting slope at higher pressure. Quantum corrections to individual energy terms are far more significant than their net effect on the melting temperatures near 50 GPa, even though lithium behaves as a quantum solid at this pressure. The scales of these corrections increase with compression. A case is made for the possibility for anomalous melting at much higher pressures, where quantum ion dynamics are expected to play a prominent role.

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.012
GPT teacher head0.299
Teacher spread0.287 · 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

Citations14
Published2016
Admission routes2
Has abstractyes

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