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Record W2006077442 · doi:10.1063/1.4832325

Efficient basis sets for non-covalent interactions in XDM-corrected density-functional theory

2013· article· en· W2006077442 on OpenAlexaff
Erin R. Johnson, Alberto Otero‐de‐la‐Roza, Stephen G. Dale, Gino A. DiLabio

Bibliographic record

VenueThe Journal of Chemical Physics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
Fundersnot available
KeywordsBasis (linear algebra)Basis setStatistical physicsIntermolecular forceDipoleDensity functional theoryPolarization (electrochemistry)Basis functionMathematicsComputational chemistryComputer sciencePhysicsChemistryQuantum mechanicsMathematical analysisMoleculeGeometryPhysical chemistry

Abstract

fetched live from OpenAlex

In the development and application of dispersion-corrected density-functional theory, the effects of basis set incompleteness have been largely mitigated through the use of very large, nearly-complete basis sets. However, the use of such large basis sets makes application of these methods inefficient for large systems. In this work, we examine a series of basis sets, including Pople-style, correlation-consistent, and polarization-consistent bases, for their ability to efficiently and accurately predict non-covalent interactions when used in conjunction with the exchange-hole dipole moment (XDM) dispersion model. We find that the polarization-consistent 2 (pc-2) basis sets, and two modifications thereof with some diffuse functions removed, give performance of comparable quality to that obtained with aug-cc-pVTZ basis sets, while being roughly 12 to 23 times faster computationally. The behavior is explained, in part, by the role of diffuse functions in recovering small density changes in the intermolecular region. The general performance of the modified basis sets is tested by application of XDM to standard intermolecular benchmark sets at, and away from, equilibrium.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.474
Threshold uncertainty score0.508

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 head0.014
GPT teacher head0.265
Teacher spread0.250 · 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

Citations41
Published2013
Admission routes1
Has abstractyes

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