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Generalization of the binary structural phase field crystal model

2017· article· en· W2766842473 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhysical Review Materials · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSolidification and crystal growth phenomena
Canadian institutionsMcGill University
FundersCanada Research Chairs
KeywordsMaterials scienceSpinodal decompositionNucleationEutectic systemBinary numberThermodynamicsPhase (matter)PrecipitationKirkendall effectSpinodalStatistical physicsAlloyMetallurgyPhysicsMathematics

Abstract

fetched live from OpenAlex

Binary phase field crystal (PFC) models have been successful in describing a broad selection of phenomena in binary alloy materials: eutectic and dendritic solidification, the Kirkendall effect, clustering, solid state precipitation, and many more. In this paper, authors present improvements to the binary structural PFC model and show that these improvements allow us to model new material phase diagrams. They apply their improved model to study the kinetics of precipitation from a liquid solution where they observe a two-step nucleation pathway. This agrees with recent experimental observations in which spinodal decomposition precedes nucleation in solute-rich domains. They also find a phenomenon not previously described in the literature in which precipitate growth is accelerated in the presence of uncrystallized, solute-rich liquid domains.

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)0.000
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 score0.001
Threshold uncertainty score0.561

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 science0.0010.000
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 head0.040
GPT teacher head0.366
Teacher spread0.325 · 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