MétaCan
Menu
Back to cohort
Record W4410477357 · doi:10.1038/s41598-025-02046-3

PuTMO3 systems crystal structures prediction and stability analysis based on first principles

2025· article· en· W4410477357 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsComputer scienceStability (learning theory)Crystal structure predictionData miningCrystal structureMachine learningCrystallographyChemistry

Abstract

fetched live from OpenAlex

Based on particle swarm optimization algorithm and first-principle calculations, the most stable structures of 10 different Pu TM O 3 systems are predicted according to their thermodynamical, mechanical and dynamical properties. The results indicate firstly all selected 50 structures are thermodynamically stable based on formation enthalpy results. 29 of the 50 structures are then predicted to be mechanically stable according to mechanical stability criterion for different structures. And then 10 of 29 structures are selected to be dynamically stable based on calculations of phonon frequency, that is, PuTiO 3 (Cc, Pca2_1), PuZnO 3 (Pca2_1, P2_1/m), PuGaO 3 (C2/m), PuMnO 3 (Pna2_1), PuNiO 3 (P4_1), PuFeO 3 (C222_1), PuVO 3 (P2_13), and PuCrO 3 (P2_13). Finally, the electronic properties of these 10 structures were calculated. All these results provide useful information to manufacture Pu-based MOX fuels by controlling impurities through formation of Pu TM O 3 compounds and the recycling of materials in recovery process.

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.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
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 score0.185
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
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 head0.012
GPT teacher head0.247
Teacher spread0.235 · 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