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Record W4399399996 · doi:10.1007/978-3-031-51443-2_5

Ab Initio Calculations

2024· book-chapter· en· W4399399996 on OpenAlexaff
Errol G. Lewars

Bibliographic record

VenueComputational Chemistry · 2024
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsTrent University
Fundersnot available
KeywordsWave functionDipoleAb initioAtomic orbitalElectronic correlationSlater determinantAb initio quantum chemistry methodsElectronChemistryAtomic physicsMolecular orbitalPhysicsComputational chemistryQuantum mechanicsMolecule

Abstract

fetched live from OpenAlex

Ab initio calculations rest on solving the Schrödinger equation; the nature of the necessary approximations determines the level of the calculation. In the simplest approach, the Hartree-Fock method, the total molecular wavefunction Ψ is approximated as a Slater determinant composed of occupied spin orbitals. To use these in practical calculations, the spatial part of the spin orbitals is approximated as a linear combination (a weighted sum) of basis functions. Electron correlation methods are also discussed. The main uses of the ab initio method are calculating molecular geometries, energies, vibrational frequencies, spectra, ionization energies and electron affinities, and properties like dipole moments which are connected with electron distribution. These calculations find theoretical and practical applications, since, for example, enzyme-substrate interactions depend on shapes and charge distributions and reaction equilibria and rates depend on energy differences, and spectroscopy plays an important role in identifying and understanding novel molecules. The visualization of calculated phenomena can be very important in interpreting results.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.136
Threshold uncertainty score0.453

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0020.002
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1360.076

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 head0.015
GPT teacher head0.258
Teacher spread0.243 · 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 designTheoretical or conceptual
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

Citations2
Published2024
Admission routes1
Has abstractyes

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