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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.

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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 1 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.

fundno affunlabeled
Quantum-chemical insights from deep tensor neural networks
Kristof T. Schütt, Farhad Arbabzadah, Stefan Chmiela, K. Müller, Alexandre Tkatchenko
2017· article· en· Nature Communications· Materials Science
machine prediction:candidate · noneconsensus · none
1,492
citations
affno abstractunlabeled
Machine learning phases of matter
Juan Carrasquilla, Roger G. Melko
2017· article· en· Nature Physics· Materials Science
machine prediction:candidate · noneconsensus · none
1,474
citations
affunlabeled
QSAR without borders
Eugene Muratov, Jürgen Bajorath, Robert P. Sheridan, Igor V. Tetko, Dmitry Filimonov, Vladimir Poroikov +13 more
2020· review· en· Chemical Society Reviews· Materials Science
machine prediction:candidate · noneconsensus · none
852
citations
afffundunlabeled
Data-Driven Strategies for Accelerated Materials Design
Robert Pollice, Gabriel dos Passos Gomes, Matteo Aldeghi, Riley J. Hickman, Mario Krenn, Cyrille Lavigne +5 more
2021· article· en· Accounts of Chemical Research· Materials Science
machine prediction:candidate · noneconsensus · none
472
citations
afffundno abstractunlabeled
Machine learning for a sustainable energy future
Zhenpeng Yao, Yanwei Lum, Andrew Johnston, L.M. Mejía-Mendoza, Xin Zhou, Yonggang Wen +3 more
2022· review· en· Nature Reviews Materials· Materials Science
machine prediction:candidate · noneconsensus · none
468
citations
affunlabeled
Phoenics: A Bayesian Optimizer for Chemistry
Florian Häse, Loı̈c M. Roch, Christoph Kreisbeck, Alán Aspuru‐Guzik
2018· article· en· ACS Central Science· Materials Science
machine prediction:candidate · noneconsensus · none
384
citations
affunlabeled
The 2019 materials by design roadmap
Kirstin Alberi, Marco Buongiorno Nardelli, Andriy Zakutayev, Luboš Mitáš, Stefano Curtarolo, Anubhav Jain +27 more
2018· article· en· Journal of Physics D Applied Physics· Materials Science
machine prediction:candidate · noneconsensus · none
343
citations
affunlabeled
Roadmap on multiscale materials modeling
E. van der Giessen, Peter A. Schultz, Nicolas Bertin, Vasily V. Bulatov, Wei Cai, Gábor Csányi +14 more
2020· article· en· Modelling and Simulation in Materials Science and Engineering· Materials Science
machine prediction:candidate · noneconsensus · none
203
citations
afffundunlabeled
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang, A. M. Elena, Dávid Péter Kovács, Janosh Riebesell +82 more
2025· article· en· The Journal of Chemical Physics· Materials Science
machine prediction:candidate · noneconsensus · none
187
citations
affunlabeled
Roadmap on Machine learning in electronic structure
Heather J. Kulik, Thomas Hammerschmidt, Jonathan Schmidt, Silvana Botti, Miguel A. L. Marques, Mario Boley +40 more
2022· article· en· Electronic Structure· Materials Science
machine prediction:candidate · noneconsensus · none
184
citations
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