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Record W2775658916 · doi:10.1021/acs.jctc.7b00802

Exploring the Accuracy of a Low Scaling Similarity Transformed Equation of Motion Method for Vertical Excitation Energies

2017· article· en· W2775658916 on OpenAlexaff
Achintya Kumar Dutta, Marcel Nooijen, Frank Neese, Róbert Izsák

Bibliographic record

VenueJournal of Chemical Theory and Computation · 2017
Typearticle
Languageen
FieldEngineering
TopicSuperconducting Materials and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsScalingSimilarity (geometry)ExcitationStructural equation modelingMotion (physics)Computer scienceEquations of motionStatistical physicsAlgorithmData miningPhysicsArtificial intelligenceClassical mechanicsMathematicsMachine learningGeometryQuantum mechanics

Abstract

fetched live from OpenAlex

The newly developed back transformed pair natural orbital based similarity transformed equation of motion (bt-STEOM) method at the coupled cluster singles and doubles level (CCSD) is combined with an appropriate modification of our earlier active space selection scheme for STEOM. The resulting method is benchmarked for valence, Rydberg, and charge transfer excited states of Thiel's test set and other test systems. The bt-PNO-STEOM-CCSD method gives very similar results to canonical STEOM-CCSD for both singlet and triplet excited states. It performs in a balanced manner for all these types of excited states, while the EOM-CCSD method performs especially well for Rydberg excited states and the CC2 method excels at obtaining accurate results for valence excited states. Both EOM-CCSD and CC2 perform worse than bt-PNO-STEOM-CCSD for charge transfer states for the test cases studied.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.110
GPT teacher head0.332
Teacher spread0.221 · 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 designSimulation or modeling
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

Citations119
Published2017
Admission routes1
Has abstractyes

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