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Record W3145299736 · doi:10.1016/j.elspec.2021.147061

2p x-ray absorption spectroscopy of 3d transition metal systems

2021· article· en· W3145299736 on OpenAlexafffund
Frank M. F. de Groot, Hebatalla Elnaggar, Federica Frati, Ru‐Pan Wang, Mario Ulises Delgado‐Jaime, Michel van Veenendaal, Javier Fernández-Rodríguez, M. W. Haverkort, Robert J. Green, G. van der Laan, Y. O. Kvashnin, Atsushi Hariki, Hidekazu Ikeno, Harry Ramanantoanina, Claude Daul, B. Delley, Michael Odelius, Marcus Lundberg, Oliver Kühn, Sergey I. Bokarev, Eric L. Shirley, John Vinson, Keith Gilmore, Mauro Stener, G. Fronzoni, Piero Decleva, Péter Krüger, Marius Retegan, Yves Joly, Christian Vorwerk, Claudia Draxl, J. J. Rehr, A. Tanaka

Bibliographic record

VenueJournal of Electron Spectroscopy and Related Phenomena · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicX-ray Spectroscopy and Fluorescence Analysis
Canadian institutionsUniversity of SaskatchewanUniversity of British Columbia
FundersDivision of Materials Sciences and EngineeringH2020 European Research CouncilPrecursory Research for Embryonic Science and TechnologyJapan Society for the Promotion of ScienceLeibniz-GemeinschaftOffice of ScienceDeutsche ForschungsgemeinschaftBasic Energy SciencesKnut och Alice Wallenbergs StiftelseNatural Sciences and Engineering Research Council of CanadaArgonne National LaboratoryU.S. Department of EnergyEuropean CommissionPaul Scherrer Institut
KeywordsMultipletComputer scienceWave functionAbsorption (acoustics)Density functional theorySpectral linePhysicsTheoretical physicsStatistical physicsAtomic physicsQuantum mechanicsOptics

Abstract

fetched live from OpenAlex

This review provides an overview of the different methods and computer codes that are used to interpret 2p x-ray absorption spectra of 3d transition metal ions. We first introduce the basic parameters and give an overview of the methods used. We start with the semi-empirical multiplet codes and compare the different codes that are available. A special chapter is devoted to the user friendly interfaces that have been written on the basis of these codes. Next we discuss the first principle codes based on band structure, including a chapter on Density Functional theory based approaches. We also give an overview of the first-principle multiplet codes that start from a cluster calculation and we discuss the wavefunction based methods, including multi-reference methods. We end the review with a discussion of the link between theory and experiment and discuss the open issues in the spectral analysis.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.005
GPT teacher head0.234
Teacher spread0.229 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations110
Published2021
Admission routes2
Has abstractyes

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