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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 2 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 affgemma · no categorygpt · no categorymodels split
Machine-learned and codified synthesis parameters of oxide materials
Edward Kim, Kevin Huang, Alex Tomala, Sara Matthews, Emma Strubell, Adam M. Saunders +2 more
2017· article· en· Scientific Data· Materials Science
machine prediction:candidate · noneconsensus · none
175
citations
affunlabeled
Toward Design of Novel Materials for Organic Electronics
Pascal Friederich, Artem Fediai, Simon Kaiser, Manuel Konrad, Nicole Jung, Wolfgang Wenzel
2019· review· en· Advanced Materials· Materials Science
machine prediction:candidate · noneconsensus · none
167
citations
affno abstractunlabeled
Materials Acceleration Platforms: On the way to autonomous experimentation
Martha M. Flores‐Leonar, L.M. Mejía-Mendoza, Andrés Aguilar‐Granda, Benjamín Sánchez-Lengeling, Hermann Tribukait, Carlos Amador‐Bedolla +1 more
2020· article· en· Current Opinion in Green and Sustainable Chemistry· Materials Science
machine prediction:candidate · noneconsensus · none
164
citations
affunlabeled
Nine questions on energy decomposition analysis
Juán Andrés, Paul W. Ayers, Roberto A. Boto, Ramon Carbó‐Dorca, Henry Chermette, Jerzy Ciosłowski +23 more
2019· article· en· Journal of Computational Chemistry· Materials Science
machine prediction:candidate · noneconsensus · none
153
citations
affno abstractunlabeled
Atomic cluster expansion: Completeness, efficiency and stability
Geneviève Dusson, Markus Bachmayr, Gábor Csányi, Ralf Drautz, Simon Etter, Cas van der Oord +1 more
2022· article· en· Journal of Computational Physics· Materials Science
machine prediction:candidate · noneconsensus · none
148
citations
affunlabeled
The Matter Simulation (R)evolution
Alán Aspuru‐Guzik, Roland Lindh, Markus Reiher
2018· review· en· ACS Central Science· Materials Science
machine prediction:candidate · noneconsensus · none
144
citations
afffundunlabeled
Deep learning and density-functional theory
Kevin Ryczko, David A. Strubbe, Isaac Tamblyn
2019· article· en· Physical review. A/Physical review, A· Materials Science
machine prediction:candidate · noneconsensus · none
125
citations
affunlabeled
The Amsterdam Modeling Suite
Evert Jan Baerends, Néstor F. Aguirre, N. Austin, Jochen Autschbach, F. Matthias Bickelhaupt, Rosa E. Bulo +28 more
2025· article· en· The Journal of Chemical Physics· Materials Science
machine prediction:candidate · noneconsensus · none
113
citations
affunlabeled
Autonomous Molecular Design: Then and Now
Tanja Dimitrov, Christoph Kreisbeck, Jill Becker, Alán Aspuru‐Guzik, Semion K. Saikin
2019· article· en· ACS Applied Materials & Interfaces· Materials Science
machine prediction:candidate · noneconsensus · none
109
citations
afffundno abstractunlabeled
Flexible automation accelerates materials discovery
Benjamin P. MacLeod, Fraser G. L. Parlane, Amanda K. Brown, Jason E. Hein, Curtis P. Berlinguette
2021· article· en· Nature Materials· Materials Science
machine prediction:candidate · noneconsensus · none
104
citations
afffundunlabeled
Hyperactive learning for data-driven interatomic potentials
Cas van der Oord, Matthias Sachs, Dávid Péter Kovács, Christoph Ortner, Gábor Cśanyi
2023· article· en· npj Computational Materials· Materials Science
machine prediction:candidate · noneconsensus · none
102
citations
affno abstractunlabeled
Artificial Intelligence in Materials Modeling and Design
Jiasheng Huang, Janet Liew, A.S. Ademiloye, K.M. Liew
2020· article· en· Archives of Computational Methods in Engineering· Materials Science
machine prediction:candidate · noneconsensus · none
93
citations
affunlabeled
A Bayesian Approach to Predict Solubility Parameters
Benjamín Sánchez-Lengeling, Loı̈c M. Roch, José Darío Perea, Stefan Langner, Christoph J. Brabec, Alán Aspuru‐Guzik
2018· article· en· Advanced Theory and Simulations· Materials Science
machine prediction:candidate · noneconsensus · none
88
citations
afffundunlabeled
The Activation-Relaxation Technique: ART Nouveau and Kinetic ART
Normand Mousseau, Laurent Karim Béland, P.E. Brommer, J.P. Joly, Fedwa El‐Mellouhi, E. Machado +2 more
2012· article· en· Journal of Atomic Molecular and Optical Physics· Materials Science
machine prediction:candidate · noneconsensus · none
87
citations
afffundunlabeled
Accelerated chemical science with AI
Seoin Back, Alán Aspuru‐Guzik, Michele Ceriotti, Ganna Gryn’ova, Bartosz A. Grzybowski, Geun Ho Gu +15 more
2023· article· en· Digital Discovery· Materials Science
machine prediction:candidate · noneconsensus · none
87
citations

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