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Record W3154453512 · doi:10.1021/acs.jpcc.0c09896

Accurate Mechanical and Electronic Properties of Spinel Nitrides from Density Functional Theory

2021· article· en· W3154453512 on OpenAlexafffund
Hang Hu, Gilles H. Peslherbe

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsNitrideDensity functional theoryAtomic orbitalMaterials scienceBand gapMetalValence (chemistry)Linear combination of atomic orbitalsElectronic structureComputational chemistryChemical physicsCondensed matter physicsChemistryNanotechnologyPhysicsElectronQuantum mechanicsMetallurgyOptoelectronicsBasis set

Abstract

fetched live from OpenAlex

The mechanical and electronic properties of both Group 14 nitrides (Si3N4, Ge3N4, and Sn3N4) and metal nitrides (Ti3N4 and Zr3N4) have been investigated with density functional theory (DFT). All nitrides feature large bulk moduli, exceeding 200 GPa, inversely proportional to the average cation–nitrogen bond length. The nitride valence band primarily involves overlap of nitrogen p-orbitals with the cation p-orbitals and metal d-orbitals for Group 14 and metal nitrides, respectively. The metal d-orbitals which essentially make up the metal nitride conduction band are accessible at a much lower energy relative to their s-p-d-hybridized Group 14 counterparts, which causes metal nitrides to have smaller band gaps than Group 14 nitrides. DFT-1/2 is shown to efficiently and properly correct the notorious electron self-interaction error associated with conventional DFT, consistently reproducing experimental band gaps within 0.1 eV. This comprehensive investigation sheds light on the similarities and differences between Group 14 and metal nitrides.

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 score0.018
Threshold uncertainty score0.400

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 head0.020
GPT teacher head0.229
Teacher spread0.210 · 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

Citations8
Published2021
Admission routes2
Has abstractyes

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