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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 6 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.

affunlabeled
Roadmap on data-centric materials science
Peter Benner, Tristan Bereau, Volker Blüm, Mario Boley, Christian Carbogno, C. Richard A. Catlow +54 more
2024· article· en· Modelling and Simulation in Materials Science and Engineering· Materials Science
machine prediction:candidate · noneconsensus · none
20
citations
affno abstractunlabeled
Bioperformance of Nitinol: Surface Tendencies
Svetlana Shabalovskaya, Jorma Ryhänen
2002· article· en· Materials science forum· Materials Science
machine prediction:candidate · noneconsensus · none
20
citations
afffundunlabeled
Twin neural network regression
Sebastian J. Wetzel, Kevin Ryczko, Roger G. Melko, Isaac Tamblyn
2022· article· en· Applied AI Letters· Materials Science
machine prediction:candidate · noneconsensus · none
19
citations
affunlabeled
Artificial intelligence for materials research at extremes
Benji Maruyama, Jason Hattrick‐Simpers, William D. Musinski, Lori Graham‐Brady, Kang Li, Jonathan Hollenbach +2 more
2022· article· en· MRS Bulletin· Materials Science
machine prediction:candidate · noneconsensus · none
19
citations
afffundunlabeled
Machine Learning Diffusion Monte Carlo Energies
Kevin Ryczko, Jaron T. Krogel, Isaac Tamblyn
2022· article· en· Journal of Chemical Theory and Computation· Materials Science
machine prediction:candidate · noneconsensus · none
19
citations
afffundunlabeled
GFlowNets for AI-driven scientific discovery
Moksh Jain, Tristan Deleu, Jason Hartford, Chenghao Liu, Álex Hernández-García, Yoshua Bengio
2023· article· en· Digital Discovery· Materials Science
machine prediction:candidate · noneconsensus · none
18
citations
fundno affunlabeled
Reducing training data needs with minimal multilevel machine learning (M3L)
Stefan Heinen, Danish Khan, Guido Falk von Rudorff, Konstantin Karandashev, Daniel Jose Arismendi Arrieta, Alastair J. A. Price +4 more
2024· article· en· Machine Learning Science and Technology· Materials Science
machine prediction:candidate · noneconsensus · none
18
citations
affno abstractunlabeled
Accelerating training of MLIPs through small-cell training
Jason Meziere, Yu Luo, Yi Xia, Laurent Karim Béland, Mark R. Daymond, Gus L. W. Hart
2023· article· en· Journal of materials research/Pratt's guide to venture capital sources· Materials Science
machine prediction:candidate · noneconsensus · none
17
citations
afffundno abstractunlabeled
Micro/nanoscale thermometry in photothermal catalysis
Cong Liu, Sanli Tang, Rui Song, Yangfan Xu, Geoffrey A. Ozin, Xiangdong Yao
2025· article· en· Joule· Materials Science
machine prediction:candidate · noneconsensus · none
16
citations

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