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Record W4415544162 · doi:10.1039/d5sc05762k

Deciphering the structure–activity–selectivity relationship of high-entropy alloys for CO <sub>2</sub> reduction <i>via</i> interpretable machine learning

2025· article· en· W4415544162 on OpenAlexaff
Jinxin Sun, Xiaokang Xu, Yuqing Mao, Anjie Chen, Shu Wang, Li Shi, Chongyi Ling, Jinlan Wang

Bibliographic record

VenueChemical Science · 2025
Typearticle
Languageen
FieldEngineering
TopicHigh Entropy Alloys Studies
Canadian institutionsMinistry of Education and Child Care
FundersNational Key Research and Development Program of ChinaBasic Research Program of Jiangsu ProvinceSix Talent Peaks Project in Jiangsu ProvinceGovernment of Jiangsu ProvinceNanjing UniversityNanjing University of Posts and TelecommunicationsSoutheast UniversityNational Natural Science Foundation of China
KeywordsWorkflowReduction (mathematics)Density functional theoryFace (sociological concept)SelectivityScaling

Abstract

fetched live from OpenAlex

The structure–activity–selectivity relation of high-entropy alloys for the CO 2 reduction reaction was systematically explored with the help of a machine learning framework and density functional theory computations.

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 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)0.001
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 score0.089
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.227
Teacher spread0.221 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2025
Admission routes1
Has abstractyes

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