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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 3 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
Identifying polymer states by machine learning
Qianshi Wei, Roger G. Melko, Jeff Z. Y. Chen
2017· article· en· Physical review. E· Materials Science
machine prediction:candidate · noneconsensus · none
79
citations
fundno affunlabeled
Application of Transformers in Cheminformatics
Kha-Dinh Luong, Ambuj K. Singh
2024· article· en· Journal of Chemical Information and Modeling· Materials Science
machine prediction:candidate · noneconsensus · none
56
citations
affunlabeled
Shared metadata for data-centric materials science
Luca M. Ghiringhelli, Carsten Baldauf, Tristan Bereau, Sándor Brockhauser, Christian Carbogno, Javad Chamanara +29 more
2023· article· en· Scientific Data· Materials Science
machine prediction:candidate · open_scienceconsensus · none
55
citations
fundno affunlabeled
ASKCOS: Open-Source, Data-Driven Synthesis Planning
Zhengkai Tu, Sourabh J. Choure, Mun Hong Fong, Jihye Roh, Itai Levin, Kevin Yu +11 more
2025· article· en· Accounts of Chemical Research· Materials Science
machine prediction:candidate · noneconsensus · none
54
citations
afffundunlabeled
A Materials Acceleration Platform for Organic Laser Discovery
Tony Wu, Andrés Aguilar‐Granda, Kazuhiro Hotta, Sahar Alasvand Yazdani, Robert Pollice, Jenya Vestfrid +9 more
2022· article· en· Advanced Materials· Materials Science
machine prediction:candidate · noneconsensus · none
54
citations

How this was built: Screen · Findings · About