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Record W4283073944 · doi:10.26434/chemrxiv-2022-13j2v

DFT Exchange: Sharing Perspectives on the Workhorse of Quantum Chemistry and Materials Science

2022· preprint· en· W4283073944 on OpenAlexafffund
Andrew M. Teale, Trygve Helgaker, Andreas Savin, Carlo Adamo, Bálint Aradi, Alexei V. Arbuznikov, Paul W. Ayers, Evert Jan Baerends, Vincenzo Barone, Patrizia Calaminici, Éric Cancès, Emily A. Carter, Pratim Kumar Chattaraj, Henry Chermette, Ilaria Ciofini, Daniel A. Crawford, Frank De Proft, John F. Dobson, Caludia Draxl, Thomas Frauenheim, Emmanuel Fromager, Patricio Fuentealba, Laura Gagliardi, Giulia Galli, Jiali Gao, Paul Geerlings, Nikitas Gidopoulous, Peter M. W. Gill, Paola Gori‐Giorgi, Andreas Görling, Tim Gould, Stefan Grimme, O. V. Gritsenko, Hans Jørgen Aa. Jensen, Erin R. Johnson, R. Jones, Martin Kaupp, Andreas M. Köster, Leeor Kronik, Anna I. Krylov, Simen Kvaal, Andre Laestadius, Mel Levy, Mathieu Lewin, Shubin Liu, Pierre‐François Loos, Neepa T. Maitra, Frank Neese, John P. Perdew, Katarzyna Pernal, Pascal Pernot, Piotr Piecuch, Elisa Rebolini, Lucia Reining, Pina Romaniello, Adrienn Ruzsinszky, Dennis R. Salahub, Matthias Scheffler, Peter Schwerdtfeger, Vicktor Staroverov, Jianwei Sun, Erik I. Tellgren, David J. Tozer, Sam Trickey, Carsten A. Ullrich, Alberto Vela, Giovanni Vignale, Tomasz A. Wesołowski, Xin Xu, Weitao Yang

Bibliographic record

VenueChemRxiv · 2022
Typepreprint
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsWestern UniversityUniversity of CalgaryDalhousie UniversityMcMaster University
FundersDivision of ChemistryAustralian Research CouncilNatural Sciences and Engineering Research Council of CanadaU.S. Department of EnergyLabEx Chimie des Systèmes ComplexesNational Natural Science Foundation of ChinaJulian Schwinger Foundation for Physics ResearchDeutsche ForschungsgemeinschaftNorges ForskningsrådAgence Nationale de la RechercheEuropean Research CouncilNational Institutes of HealthNational Science Foundation
KeywordsSnapshot (computer storage)Density functional theoryComputer scienceField (mathematics)ChemistryNanotechnologyComputational chemistryMaterials scienceMathematicsDatabasePure mathematics

Abstract

fetched live from OpenAlex

In this paper, the history, present status, and future of density-functional theory (DFT) is informally reviewed and discussed by 70 workers in the field, including molecular scientists, materials scientists, method developers and practitioners. The format of the paper is that of a roundtable discussion, in which the participants express and exchange views on DFT in the form of 300 individual contributions, formulated as responses to a preset list of 26 questions. Supported by a bibliography of 776 entries, the paper represents a broad snapshot of DFT, anno 2022.

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.

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)0.019
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.006
Scholarly communication0.0080.014
Open science0.0020.011
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0210.004

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 head0.025
GPT teacher head0.292
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreCommentary

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

Citations26
Published2022
Admission routes2
Has abstractyes

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