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Record W6999728284

The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

2022· article· en· W6999728284 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueApollo (University of Cambridge) · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsnot available
FundersFAS Division of Science, Harvard UniversityDivision of Materials ResearchMaterials Research Science and Engineering Center, Harvard UniversityAstraZenecaEngineering and Physical Sciences Research CouncilLeverhulme TrustOffice of ScienceAdvanced Scientific Computing ResearchU.S. Department of EnergyScience and Technology Facilities CouncilBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaDell EMCHarvard UniversityNational Science Foundation
KeywordsWork (physics)FrontierScience and engineeringService (business)Research councilResearch programGraduate researchEngineering research
DOInot available

Abstract

fetched live from OpenAlex

Acknowledgements: This work was performed using resources provided by the Cambridge Service for Data Driven Discovery (CSD3), which is operated by the University of Cambridge Research Computing Service (www.csd3.cam.ac.uk) provided by Dell EMC and Intel using Tier-2 funding from the Engineering and Physical Sciences Research Council (capital grant number EP/T022159/1) and DiRAC funding from the Science and Technology Facilities Council (www.dirac.ac.uk). D.P.K. acknowledges support from AstraZeneca and the Engineering and Physical Sciences Research Council. C.O. is supported by Leverhulme Research Project grant number RPG-2017-191 and by the Natural Sciences and Engineering Research Council of Canada (NSERC) under funding reference number IDGR019381. Work at Harvard University was supported by Bosch Research, the US Department of Energy, Office of Basic Energy Sciences, under award number DE-SC0022199, the Integrated Mesoscale Architectures for Sustainable Catalysis (IMASC), an Energy Frontier Research Center, under award number DE-SC0012573 and by the NSF through Harvard University Materials Research Science and Engineering Center grant number DMR-2011754. A.M. is supported by US Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Computational Science Graduate Fellowship under award number DE-SC0021110. We acknowledge computing resources provided by the Harvard University FAS Division of Science Research Computing Group.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.207
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it