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Record W2021563945 · doi:10.1021/jp051616j

Impacts of Quantization on the Properties of Liquid Water

2005· article· en· W2021563945 on OpenAlexaff
Lisandro Hernández de la Peña, M. Shajahan G. Razul, Peter G. Kusalik

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsQuantization (signal processing)Liquid waterQuantumQuantum tunnellingMolecular dynamicsChemical physicsMoleculeHydrogen bondQuantum fluctuationMaterials scienceStatistical physicsPhysicsCondensed matter physicsThermodynamicsQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

The results of classical and quantum simulations of liquid water over a wide range of temperatures are compared to probe the impact of quantization on the properties of liquid water. We show that, when treated quantum mechanically, water molecules have an enhanced probability of accessing nontetrahedral coordination in the local three-dimensional structure. We discuss how this enhanced probability, also called "effective tunneling", is related to the dynamics of the hydrogen-bond breaking and molecular diffusion in the liquid. We explore in detail how local molecular environments affect the manifestation of quantum effects and identify a previously unreported and apparently unique behavior of the quantum mechanical uncertainty of the water molecule as a function of temperature. The nonmonotonic behavior of the quantum mechanical uncertainty with temperature is shown to be due to the notable strength of the water-water interaction in the condensed phase and becomes further evidence of the importance of the water structure in the properties of this ubiquitous liquid.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.245
Teacher spread0.232 · 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 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

Citations19
Published2005
Admission routes1
Has abstractyes

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