MétaCan
Menu
Back to cohort
Record W2019341662 · doi:10.1063/1.1540742

<i>Ab initio</i> study of elastic properties of Ir and Ir3X compounds

2003· article· en· W2019341662 on OpenAlexaff
Kuiying Chen, L.R. Zhao, John S. Tse

Bibliographic record

VenueJournal of Applied Physics · 2003
Typearticle
Languageen
FieldEngineering
TopicIntermetallics and Advanced Alloy Properties
Canadian institutionsSteacie Institute for Molecular SciencesNational Research Council Canada
FundersU.S. Department of Defense
KeywordsBulk modulusTetragonal crystal systemMaterials scienceShear modulusLattice constantAb initioDensity functional theoryPoisson's ratioThermodynamicsIntermetallicElastic modulusAb initio quantum chemistry methodsCrystallographyComputational chemistryChemistryComposite materialCrystal structureDiffractionAlloyPoisson distributionPhysicsOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

Elastic constants and moduli of face-centered cubic Ir and its L12 intermetallic compounds Ir3X (X=Ti, Ta, Nb, Zr, Hf, V) have been determined using ab initio density functional theory calculations within the generalized gradient approximation. With the tetragonal, trigonal, and isotropical lattice distortions, elastic constants C11, C12, C44, and bulk modulus B are derived from the second derivative of the total energy as a function of volume. The calculated Young’s modulus E, shear modulus G, Poisson’s ratio ν, and the ratio RG/B of G over B are then used to examine mechanical properties of Ir and Ir3X compounds. By analyzing RG/B and Cauchy pressure C12–C44, the brittle-ductile behavior of the materials is assessed. Based on the modulus difference ΔG between the γ matrix (Ir) and γ′ precipitates (Ir3X), the γ′ strengthening effect in the γ matrix is studied.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.199
Teacher spread0.181 · 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

Citations140
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Applied PhysicsSame topicIntermetallics and Advanced Alloy PropertiesFrench-language works237,207