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Record W4387048653 · doi:10.26434/chemrxiv-2023-1lprg

Chemical bonding and the role of node-induced electron confinement

2023· preprint· en· W4387048653 on OpenAlexaff
Alistair J. Sterling, Daniel S. Levine, Abdulrahman Aldossary, Martin Head‐Gordon

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldChemistry
TopicCrystallography and molecular interactions
Canadian institutionsUniversity of Toronto
FundersBasic Energy SciencesDivision of ChemistryU.S. Department of EnergyNational Science Foundation
KeywordsChemical bondChemical physicsPauli exclusion principleDelocalized electronElectronegativityPi bondElectronThree-center two-electron bondChemistryMolecular orbital theoryAtomic orbitalMoleculeNanotechnologyBond orderAtomic physicsComputational chemistryPhysicsMaterials scienceValence bond theoryQuantum mechanicsBond length

Abstract

fetched live from OpenAlex

The chemical bond is the cornerstone of chemistry, providing a conceptual framework to understand and predict the behavior of molecules in complex systems. However, the fundamental origin of chemical bonding remains controversial, and has been responsible for fierce debate over the past century. Here we present a unified theory of bonding, using a separation of electron delocalization effects from orbital relaxation to identify four mechanisms – node-induced electron confinement, Pauli repulsion, orbital contraction and polarization – that each modulate kinetic energy during bond formation. Through analysis of a series of archetypal bonds, we show that electron delocalization is not the universal driving force for bonding. Instead, an exquisite balance of delocalizing and localizing effects are dictated simply by atomic electron configurations, nodal structure and electronegativities. The utility of this unified bonding theory is demonstrated by its application to explain observed trends in bond strengths throughout the periodic table, including main group and transition metal elements.

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 score0.021
Threshold uncertainty score0.683

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 integrity0.0000.001
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 head0.258
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

Citations1
Published2023
Admission routes1
Has abstractyes

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