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Record W3215117564

Adversarial Generation of Mesoscale Surfaces from Small-Scale Chemical Motifs

2020· article· en· W3215117564 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Physical Chemistry · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsNational Research Council CanadaOntario Tech University
Fundersnot available
KeywordsChemistryScale (ratio)Statistical physicsGenerative grammarGenerator (circuit theory)Chemical spaceSpace (punctuation)Artificial intelligencePhysicsThermodynamicsComputer scienceQuantum mechanicsPower (physics)
DOInot available

Abstract

fetched live from OpenAlex

We demonstrate the use of a regressive upscaling generative adversarial network (RUGAN) as an effective way to sample state space for hexagonal porous graphene sheets. The RUGAN can, after being trained on a set of small-scale examples, generate new, energetically relevant microstates (atomic configurations). The RUGAN can generate configurations across a continuum of total energy values and produce configurations at requested energy values. The microstates produced respect periodic boundary conditions, and importantly, the fully convolutional nature of the generator allows the generation of arbitrarily large microstates, after being trained on only a small-scale data set.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.023
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.238
Teacher spread0.217 · 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