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Record W4378770453 · doi:10.17863/cam.102005

How to verify the precision of density-functional-theory implementations via reproducible and universal workflows

2023· preprint· en· W4378770453 on OpenAlexaff
Emanuele Bosoni, Louis Beal, Marnik Bercx, Peter Blaha, Stefan Blügel, J. D. Broder, Martin Callsen, Stefaan Cottenier, Augustin Degomme, Vladimir Dikan, Kristjan Eimre, Espen Flage−Larsen, Marco Fornari, Alberto Garcı́a, Luigi Genovese, Matteo Giantomassi, Sebastiaan P. Huber, Henning Janssen, Georg Kastlunger, Matthias Krack, Georg Kresse, Thomas D. Kühne, Kurt Lejaeghere, Georg K. H. Madsen, Martijn Marsman, Nicola Marzari, Gregor Michalicek, Hossein Mirhosseini, Tiziano Müller, Guido Petretto, Chris J. Pickard, Samuel Poncé, Gian‐Marco Rignanese, Oleg Rubel, Thomas Ruh, Michael Sluydts, Danny E. P. Vanpoucke, Sudarshan Vijay, Michael Wolloch, Daniel Wortmann, Aliaksandr V. Yakutovich, Ju‐Song Yu, Austin Zadoks, Bonan Zhu, Giovanni Pizzi

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsMcMaster University
FundersAgencia Estatal de InvestigaciónNational Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel MaterialsEngineering and Physical Sciences Research CouncilGauss Centre for SupercomputingUniversität PaderbornRWTH Aachen UniversityCommissariat à l'Énergie Atomique et aux Énergies AlternativesDanmarks Tekniske UniversitetVillum FondenForschungszentrum JülichVlaams Supercomputer CentrumFonds Wetenschappelijk OnderzoekPartnership for Advanced Computing in Europe AISBLUniversiteit GentEuropean Regional Development FundEuropean CommissionVlaamse regeringFonds De La Recherche Scientifique - FNRSSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungUniversität WienNational Science Foundation
KeywordsComputer sciencePseudopotentialDensity functional theoryRange (aeronautics)WorkflowReliability (semiconductor)TransferabilityPeriodic boundary conditionsProtocol (science)ComputationImplementationTheoretical computer scienceAlgorithmBoundary value problemPhysicsProgramming languageMaterials scienceQuantum mechanicsMachine learning

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.138
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.970
Threshold uncertainty score0.161

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.138
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0020.002
Science and technology studies0.0030.006
Scholarly communication0.0080.013
Open science0.0070.007
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0110.005

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 head0.077
GPT teacher head0.228
Teacher spread0.151 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designSimulation or modeling
DomainReproducibility
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractno

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