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Record W2460103580 · doi:10.1021/acs.inorgchem.6b00881

Prediction of Host–Guest Na–Fe Intermetallics at High Pressures

2016· article· en· W2460103580 on OpenAlexaff
Yuanyuan Zhou, Hui Wang, Chunye Zhu, Hanyu Liu, John S. Tse, Yanming Ma

Bibliographic record

VenueInorganic Chemistry · 2016
Typearticle
Languageen
FieldEngineering
TopicIntermetallics and Advanced Alloy Properties
Canadian institutionsUniversity of Saskatchewan
FundersMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsIntermetallicChemistryStoichiometryPhase (matter)CrystallographyDensity functional theoryHigh pressureGroup (periodic table)Chemical physicsComputational chemistryThermodynamicsPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

High pressure can fundamentally alter the electronic structure of elemental metals, leading to the unexpected formation of intermetallics with unusual structural features. In the present study, the phase stabilities and structural changes of Na-Fe intermetallics under pressure were studied using unbiased structure searching methods, combined with density functional theory calculations. Two intermetallics with stoichiometries Na3Fe and Na4Fe are found to be thermodynamically stable at pressures above 120 and 155 GPa, respectively. An interesting structural feature is that both have form a host-guest-like structure with Na sublattices constructed from small and large polygons similar to the host framework of the self-hosting incommensurate phases observed in Group I and II elements. Apart from the one-dimensional (1D) Fe chains running through the large channels, more interestingly, electrides are found to localize in the small channels between the layers. Electron topological analysis shows secondary bonding interactions between the Fe atoms and the interstitial electrides help to stabilize these structures.

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 score0.002

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 studies0.0000.000
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 head0.008
GPT teacher head0.174
Teacher spread0.165 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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