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Record W4414151290 · doi:10.1016/j.nimb.2025.165843

Embedded atom method potentials for Zr reparameterized at short distance

2025· article· en· W4414151290 on OpenAlexafffund
Amir Ghorbani, Artur Tamm, Laurent Karim Béland

Bibliographic record

VenueNuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaQueen's University
KeywordsAtom (system on chip)Work (physics)Context (archaeology)Field (mathematics)Chain (unit)

Abstract

fetched live from OpenAlex

Three embedded atom method (EAM) interatomic potentials were reparameterized to improve their ability to describe primary damage production in Zr under irradiation. Both the two-body and embedding energy functions of these EAM potentials were refitted with the goal of improving the description of Zr atoms at short distance and under high pressure, while keeping the near-equilibrium properties of the material unchanged. The reparameterization was informed by density functional theory calculations. Namely, the equation of state of Zr and the energy of embedded dimers (also known as applying quasi-static drag) were calculated and used as targets for the fit. The reparameterized potentials have similar point defect formation energies and elastic constants as compared to the original EAM potentials. On the other hand, reparameterization had a significant impact on displacement threshold energies (TDEs). In particular, reparameterization led the 0001 TDE as predicted by all three EAM potentials to become much closer to a previously reported ab initio TDE value.

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)0.001
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.056
GPT teacher head0.437
Teacher spread0.381 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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