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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Machine Learning in Materials Science
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,108 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,108 works in the cohort · of 4,299,418page 4 of 23

Labels cover 4 of 1,108 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 1,108 of 1,108 works in this cohort. Predictions are machine_predicted_unvalidated. The Gemma side is a direct model label for every work (title-only); the Codex side is a distilled, calibrated classifier. Candidate is the union; consensus is the intersection.

afffundunlabeled
Can large language models understand molecules?
Shaghayegh Sadeghi, Alan Bui, Ali Forooghi, Jianguo Lü, Alioune Ngom
2024· article· en· BMC Bioinformatics· Materials Science
machine prediction:candidate · noneconsensus · none
40
citations
afffundunlabeled
Interpretable discovery of semiconductors with machine learning
Hitarth Choubisa, Petar Todorović́, João M. Pina, Darshan H. Parmar, Ziliang Li, Oleksandr Voznyy +2 more
2023· article· en· npj Computational Materials· Materials Science
machine prediction:candidate · noneconsensus · none
39
citations
afffundunlabeled
Tensor-Reduced Atomic Density Representations
James P. Darby, Dávid Péter Kovács, Ilyes Batatia, A. Miguel, Gus L. W. Hart, Christoph Ortner +1 more
2023· article· en· Physical Review Letters· Materials Science
machine prediction:candidate · noneconsensus · none
38
citations
afffundunlabeled
El Agente: An autonomous agent for quantum chemistry
Yushi Zou, Austin H. Cheng, Abdulrahman Aldossary, Shi Xuan Leong, Jorge A. Campos-Gonzalez-Angulo, Changhyeok Choi +9 more
2025· article· es· Matter· Materials Science
machine prediction:candidate · noneconsensus · none
37
citations
affunlabeled
Intelligent Machine Learning: Tailor-Making Macromolecules
Yousef Mohammadi, Mohammad Reza Saeb, Alexander Penlidis, Esmaiel Jabbari, Florian J. Stadler, Philippe Zinck +1 more
2019· article· en· Polymers· Materials Science
machine prediction:candidate · noneconsensus · none
35
citations
affunlabeled
Alchemical Normal Modes Unify Chemical Space
Stijn Fias, K. Y. Samuel Chang, O. Anatole von Lilienfeld
2018· article· en· The Journal of Physical Chemistry Letters· Materials Science
machine prediction:candidate · noneconsensus · none
34
citations
affunlabeled
The Truth, The Whole Truth, and Nothing But the Truth
Stephen M. Blackburn, Amer Diwan, Matthias Hauswirth, Peter F. Sweeney, José Nelson Amaral, Tim Brecht +13 more
2016· article· en· ACM Transactions on Programming Languages and Systems· Materials Science
machine prediction:candidate · noneconsensus · none
29
citations

How this was built: Screen · Findings · About