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Record W2324913148 · doi:10.1021/ct1004577

Computing Second-Order Functional Derivatives with Respect to the External Potential

2010· article· en· W2324913148 on OpenAlexaff
Nick Sablon, Frank De Proft, Paul W. Ayers, Paul Geerlings

Bibliographic record

VenueJournal of Chemical Theory and Computation · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDensity functional theoryBenchmark (surveying)Perturbation theory (quantum mechanics)Applied mathematicsBasis (linear algebra)Limit (mathematics)Set (abstract data type)Kernel (algebra)Exact solutions in general relativityBasis setOrder (exchange)Series (stratigraphy)Statistical physicsMathematicsComputer sciencePhysicsMathematical analysisQuantum mechanicsDiscrete mathematics

Abstract

fetched live from OpenAlex

Following the increasing interest in the higher-order (functional) derivatives of conceptual density functional theory, we developed and implemented a method for calculating second-order functional derivatives with respect to the external potential. Our method is theoretically exact but involves two numerical approximations: the functional derivatives are expanded in a basis set, and the values of the corresponding expansion coefficients are determined by probing the molecular environment by a finite set of external potential perturbations. Exact solutions are obtained only in the limit of a complete basis set and an infinite number of distinct perturbations. We use this method to compute the atom-condensed linear response kernel for a series of six molecules and show that the results are comparable to the ones obtained by a previously proposed, approximate approach from second-order perturbation theory. The numerical error of the current implementation is about 0.01 au. Because the present method gives exact or quasi-exact solutions, it can be used as a benchmark against which approximate approaches are assessed.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.007
GPT teacher head0.251
Teacher spread0.244 · 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 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

Citations47
Published2010
Admission routes1
Has abstractyes

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