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Record W4224105563 · doi:10.1002/cphc.202200172

Cover Feature: Exploring the Effects of Ionic Defects on the Stability of CsPbI<sub>3</sub> with a Deep Learning Potential (ChemPhysChem 7/2022)

2022· article· en· W4224105563 on OpenAlex
Weijie Yang, Jiajia Li, Xuelu Chen, Yajun Feng, Chongchong Wu, Ian D. Gates, Zhengyang Gao, Xunlei Ding, Jianxi Yao, Hao Li

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

VenueChemPhysChem · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCover (algebra)Feature (linguistics)Ionic bondingStability (learning theory)ChemistryNanotechnologyComputer scienceArtificial intelligenceComputational chemistryMaterials scienceMachine learningEngineeringPhilosophyIonOrganic chemistryMechanical engineering

Abstract

fetched live from OpenAlex

The Cover Feature illustrates the concept of using machine learning and data science to predict the energies and properties of CsPbI3 materials. More information can be found in the Research Article by Z. Gao, X. Ding, J. Yao, H. Li and co-workers.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.009
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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