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Record W583506532

Semidefinite programs in electronic structure calculation(Mathematics of Optimization : Methods and Practical Solutions)

2005· article· en· W583506532 on OpenAlexfundno aff
Mituhiro Fukuda, Bastiaan J. Braams, Maho Nagata, Michael L. Overton, J. K. Percus, Makoto Yamashita, Zhengji Zhao

Bibliographic record

VenueKyoto University Research Information Repository (Kyoto University) · 2005
Typearticle
Languageen
FieldEngineering
TopicAdvanced Theoretical and Applied Studies in Material Sciences and Geometry
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceYork UniversityU.S. Department of EnergyMinistry of Education, Culture, Sports, Science and TechnologyNational Science Foundation
KeywordsSemidefinite programmingMathematical optimizationMathematicsComputer scienceApplied mathematicsCalculus (dental)
DOInot available

Abstract

fetched live from OpenAlex

It has been a long-time dream in electronic structure theory in physical chem- $\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{y}/\mathrm{c}\mathrm{h}\mathrm{e}\mathrm{m}\mathrm{i}\mathrm{c}\mathrm{a}\mathrm{l}$ physics to compute ground state energies of atomic and molecular systems by employing a variational approach in which the two-body reduced den- sity matrix (RDM) is the unknown variable.Realization of the RDM approach has benefited greatly from recent developments in semidefinite programming (SDP).We present the actual state of this new application of SDP as well as the formulations of these SDPs, which can be arbitrarily large.Numerical experiments using different SDP codes and formulations are given in order to seek for the best choices.The RDM method has several advantages including robustness and provision of high accuracy compared to traditional electronic structure methods, although its computational time and memory consum ption are still extremely large.

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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.291
Teacher spread0.265 · 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 teacher head, 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

Citations0
Published2005
Admission routes1
Has abstractyes

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